3091 lines
228 KiB
Plaintext
3091 lines
228 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "HkkjbMsIzjRO",
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"outputId": "649333f7-5d90-446d-e67c-07cc2af89fca"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.32.3)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.3.2)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.7)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (2.0.7)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2024.6.2)\n",
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"Requirement already satisfied: jieba in /usr/local/lib/python3.10/dist-packages (0.42.1)\n",
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"Requirement already satisfied: pypinyin in /usr/local/lib/python3.10/dist-packages (0.51.0)\n",
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"Using pip 23.1.2 from /usr/local/lib/python3.10/dist-packages/pip (python 3.10)\n",
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"Looking in indexes: https://download.pytorch.org/whl/cu118\n",
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"Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.1+cu118)\n",
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"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.14.0)\n",
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"Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.1)\n",
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"Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12.1)\n",
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"Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
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"Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.8.89 in /usr/local/lib/python3.10/dist-packages (from torch) (11.8.89)\n",
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"Requirement already satisfied: nvidia-cuda-runtime-cu11==11.8.89 in /usr/local/lib/python3.10/dist-packages (from torch) (11.8.89)\n",
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"Requirement already satisfied: nvidia-cuda-cupti-cu11==11.8.87 in /usr/local/lib/python3.10/dist-packages (from torch) (11.8.87)\n",
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"Requirement already satisfied: nvidia-cudnn-cu11==8.7.0.84 in /usr/local/lib/python3.10/dist-packages (from torch) (8.7.0.84)\n",
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"Requirement already satisfied: nvidia-cublas-cu11==11.11.3.6 in /usr/local/lib/python3.10/dist-packages (from torch) (11.11.3.6)\n",
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"Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /usr/local/lib/python3.10/dist-packages (from torch) (10.9.0.58)\n",
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"Requirement already satisfied: nvidia-curand-cu11==10.3.0.86 in /usr/local/lib/python3.10/dist-packages (from torch) (10.3.0.86)\n",
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"Requirement already satisfied: nvidia-cusolver-cu11==11.4.1.48 in /usr/local/lib/python3.10/dist-packages (from torch) (11.4.1.48)\n",
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"Requirement already satisfied: nvidia-cusparse-cu11==11.7.5.86 in /usr/local/lib/python3.10/dist-packages (from torch) (11.7.5.86)\n",
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"Requirement already satisfied: nvidia-nccl-cu11==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch) (2.20.5)\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchtext) (1.25.2)\n",
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"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
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"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
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"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
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"Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
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"Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
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"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n",
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"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (2023.6.0)\n",
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"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (4.12.1)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.6.2)\n",
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"Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (7.34.0)\n",
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"Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (3.0.11)\n",
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"Collecting jupyter-client (from ipykernel>=4.5.1->ipywidgets)\n",
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"Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (2.22)\n",
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|||
|
"Requirement already satisfied: anyio<4,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (3.7.1)\n",
|
|||
|
"Requirement already satisfied: websocket-client in /usr/local/lib/python3.10/dist-packages (from jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.8.0)\n",
|
|||
|
"Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (3.7)\n",
|
|||
|
"Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.3.1)\n",
|
|||
|
"Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.2.1)\n",
|
|||
|
"Installing collected packages: jupyter-client\n",
|
|||
|
" Attempting uninstall: jupyter-client\n",
|
|||
|
" Found existing installation: jupyter_client 8.6.2\n",
|
|||
|
" Uninstalling jupyter_client-8.6.2:\n",
|
|||
|
" Successfully uninstalled jupyter_client-8.6.2\n",
|
|||
|
"Successfully installed jupyter-client-7.4.9\n",
|
|||
|
"Requirement already satisfied: jupyter_core in /usr/local/lib/python3.10/dist-packages (5.7.2)\n",
|
|||
|
"Requirement already satisfied: jupyter_client in /usr/local/lib/python3.10/dist-packages (7.4.9)\n",
|
|||
|
"Collecting jupyter_client\n",
|
|||
|
" Using cached jupyter_client-8.6.2-py3-none-any.whl (105 kB)\n",
|
|||
|
"Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter_core) (4.2.2)\n",
|
|||
|
"Requirement already satisfied: traitlets>=5.3 in /usr/local/lib/python3.10/dist-packages (from jupyter_core) (5.7.1)\n",
|
|||
|
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from jupyter_client) (2.8.2)\n",
|
|||
|
"Requirement already satisfied: pyzmq>=23.0 in /usr/local/lib/python3.10/dist-packages (from jupyter_client) (24.0.1)\n",
|
|||
|
"Requirement already satisfied: tornado>=6.2 in /usr/local/lib/python3.10/dist-packages (from jupyter_client) (6.3.3)\n",
|
|||
|
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->jupyter_client) (1.16.0)\n",
|
|||
|
"Installing collected packages: jupyter_client\n",
|
|||
|
" Attempting uninstall: jupyter_client\n",
|
|||
|
" Found existing installation: jupyter_client 7.4.9\n",
|
|||
|
" Uninstalling jupyter_client-7.4.9:\n",
|
|||
|
" Successfully uninstalled jupyter_client-7.4.9\n",
|
|||
|
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
|
|||
|
"notebook 6.5.5 requires jupyter-client<8,>=5.3.4, but you have jupyter-client 8.6.2 which is incompatible.\u001b[0m\u001b[31m\n",
|
|||
|
"\u001b[0mSuccessfully installed jupyter_client-8.6.2\n",
|
|||
|
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.0.3)\n",
|
|||
|
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
|
|||
|
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)\n",
|
|||
|
"Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
|
|||
|
"Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.25.2)\n",
|
|||
|
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
|
|||
|
"\u001b[31mERROR: Could not find a version that satisfies the requirement re (from versions: none)\u001b[0m\u001b[31m\n",
|
|||
|
"\u001b[0m\u001b[31mERROR: No matching distribution found for re\u001b[0m\u001b[31m\n",
|
|||
|
"\u001b[0mRequirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
|
|||
|
"Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.25.2)\n",
|
|||
|
"Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.10.1)\n",
|
|||
|
"Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
|
|||
|
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n",
|
|||
|
"Requirement already satisfied: scipy==1.10.1 in /usr/local/lib/python3.10/dist-packages (1.10.1)\n",
|
|||
|
"Requirement already satisfied: numpy<1.27.0,>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scipy==1.10.1) (1.25.2)\n",
|
|||
|
"Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (4.3.2)\n",
|
|||
|
"Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.25.2)\n",
|
|||
|
"Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.10.1)\n",
|
|||
|
"Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim) (6.4.0)\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"%pip install --upgrade requests\n",
|
|||
|
"%pip install jieba\n",
|
|||
|
"%pip install pypinyin\n",
|
|||
|
"%pip install -v torch torchtext --index-url https://download.pytorch.org/whl/cu118\n",
|
|||
|
"%pip install chardet\n",
|
|||
|
"%pip install transformers\n",
|
|||
|
"%pip install ipywidgets\n",
|
|||
|
"%pip install --upgrade jupyter_core jupyter_client\n",
|
|||
|
"%pip install pandas\n",
|
|||
|
"%pip install re\n",
|
|||
|
"%pip install scikit-learn\n",
|
|||
|
"%pip install scipy==1.10.1\n",
|
|||
|
"%pip install gensim"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 60,
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "FfCVzxgrzjRQ",
|
|||
|
"outputId": "78fe544e-604f-427f-d5fa-f7f9dd569d73"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"Enabling notebook extension jupyter-js-widgets/extension...\n",
|
|||
|
"Paths used for configuration of notebook: \n",
|
|||
|
" \t/root/.jupyter/nbconfig/notebook.json\n",
|
|||
|
"Paths used for configuration of notebook: \n",
|
|||
|
" \t\n",
|
|||
|
" - Validating: \u001b[32mOK\u001b[0m\n",
|
|||
|
"Paths used for configuration of notebook: \n",
|
|||
|
" \t/root/.jupyter/nbconfig/notebook.json\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"!jupyter nbextension enable --py widgetsnbextension\n",
|
|||
|
"import jieba\n",
|
|||
|
"import pypinyin\n",
|
|||
|
"import torch\n",
|
|||
|
"from transformers import AutoTokenizer, AutoModel\n",
|
|||
|
"import pandas\n",
|
|||
|
"import re\n",
|
|||
|
"from sklearn.model_selection import train_test_split\n",
|
|||
|
"from sklearn.datasets import load_iris\n",
|
|||
|
"import numpy"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 61,
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "uJ7-RpaIzjRR",
|
|||
|
"outputId": "16911b9c-7ff0-45bc-8cb5-ec4bdd047489"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"True\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"print(torch.cuda.is_available())\n",
|
|||
|
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "3HrRD1gIzjRR"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"## Normalizacja wejścia - pozbycie się spacji i znaków innych niż chińskie (interpunkcyjnych).\n",
|
|||
|
"### TODO - przepisać używając słownika znaków chińskich?"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 62,
|
|||
|
"metadata": {
|
|||
|
"id": "wlFuEbDozjRS"
|
|||
|
},
|
|||
|
"outputs": [],
|
|||
|
"source": [
|
|||
|
"# lower() - male litery\n",
|
|||
|
"# strip() - bez krancowych znakow niedrukowalnych\n",
|
|||
|
"# bez znakow interpunkcyjnych\n",
|
|||
|
"def normalizeString(s):\n",
|
|||
|
" s = s.lower().strip()\n",
|
|||
|
" s = re.sub(r\"([.!?])\", r\"\", s)\n",
|
|||
|
" s = re.sub(r\"([,;:-])\", r\"\", s)\n",
|
|||
|
" s = re.sub(r\"([。,?”“《》·、!:;π…ㄚ])\", r\"\", s)\n",
|
|||
|
" s = re.sub(r\"([/])\", r\"\", s)\n",
|
|||
|
" s = re.sub(r\"(['\\\"])\", r\" \", s)\n",
|
|||
|
" return s.strip()\n",
|
|||
|
"\n",
|
|||
|
"def normalizeChinese(s):\n",
|
|||
|
" s = normalizeString(s)\n",
|
|||
|
" pom = \"\"\n",
|
|||
|
" for c in s:\n",
|
|||
|
" if c != \" \":\n",
|
|||
|
" pom+=c\n",
|
|||
|
" #pom+=\" \"\n",
|
|||
|
" return pom.strip()"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "cT_IzrDRzjRS"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"## Wczytanie zbioru danych. https://www.kaggle.com/datasets/marquis03/chinese-couplets-dataset"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 63,
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "9PzMlBEJzjRT",
|
|||
|
"outputId": "869fe5f6-55cc-4f1b-fad0-0cfcce572422"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"腾飞上铁锐意改革谋发展勇当千里马\n",
|
|||
|
"和谐南供安全送电保畅通争做领头羊\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"fixed_couplets_in = pandas.read_csv(\"fixed_couplets_in.txt\", sep=\"\\t\", names=[\"in\"], header=None)\n",
|
|||
|
"fixed_couplets_out = pandas.read_csv(\"fixed_couplets_out.txt\", sep=\"\\t\", names=[\"out\"], header=None)\n",
|
|||
|
"\n",
|
|||
|
"normalized_fixed_couplets_in=[]\n",
|
|||
|
"for _ in fixed_couplets_in[\"in\"]:\n",
|
|||
|
" normalized_fixed_couplets_in.append(normalizeChinese(_))\n",
|
|||
|
"normalized_fixed_couplets_out=[]\n",
|
|||
|
"for _ in fixed_couplets_out[\"out\"]:\n",
|
|||
|
" normalized_fixed_couplets_out.append(normalizeChinese(_))\n",
|
|||
|
"\n",
|
|||
|
"print(normalized_fixed_couplets_in[0])\n",
|
|||
|
"print(normalized_fixed_couplets_out[0])"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 64,
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "eqT5slASzjRT",
|
|||
|
"outputId": "962e9a92-a54b-4d0c-85d1-8ffef0b32d79"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
" in out\n",
|
|||
|
"0 腾飞上铁锐意改革谋发展勇当千里马 和谐南供安全送电保畅通争做领头羊\n",
|
|||
|
"1 风弦未拨心先乱 夜幕已沉梦更闲\n",
|
|||
|
"2 花梦粘于春袖口 莺声溅落柳枝头\n",
|
|||
|
"3 晋世文章昌二陆 魏家词赋重三曹\n",
|
|||
|
"4 一句相思吟岁月 千杯美酒醉风情\n",
|
|||
|
"... ... ...\n",
|
|||
|
"744910 半榻诗书盈陋室 一墙字画靓寒庐\n",
|
|||
|
"744911 借角青山埋姓字 掬壶明月洗尘心\n",
|
|||
|
"744912 苑内尽天姿锦窠仙髻无双艳 亭前多国色金粉紫檀第一香\n",
|
|||
|
"744913 浩淼洞庭极目天为界 安闲钓叟静心孰羡鱼\n",
|
|||
|
"744914 志踏云梯能揽月 坚磨铁棒可成针\n",
|
|||
|
"\n",
|
|||
|
"[744915 rows x 2 columns]\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"fixed_couplets = pandas.DataFrame(\n",
|
|||
|
" {\"in\": normalized_fixed_couplets_in,\n",
|
|||
|
" \"out\": normalized_fixed_couplets_out\n",
|
|||
|
" }\n",
|
|||
|
" )\n",
|
|||
|
"print(fixed_couplets)"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "_q-zfMLyzjRT"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Odrzucenie 99% danych - więcej niż 5% zajmuje całą pamięć i wywala program.\n",
|
|||
|
"### Podział danych na zbiór treningowy i testowy."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 65,
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "1G6CtJ4mzjRU",
|
|||
|
"outputId": "4ca8ebb3-1df0-4888-f874-f2783fb2e409"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
" in out\n",
|
|||
|
"567354 宇高炎暑净 秋爽飒风来\n",
|
|||
|
"118920 忧乐关天下 安危系一身\n",
|
|||
|
"738591 一盏相思量寂寞 三分惆怅兑凄凉\n",
|
|||
|
"509346 孝驻锦绣城喜吕梁歌飞春融三晋千秋画 义圆和谐梦看汾河景瑞水起九州万卷诗\n",
|
|||
|
"75388 春临八桂海豚舞 福满九州彩凤飞\n",
|
|||
|
"... ... ...\n",
|
|||
|
"116492 创中华古老文明当同日月齐辉功垂万代 启黎庶鸿蒙草昧是与山河并寿德颂千秋\n",
|
|||
|
"91658 纠缠海角指相思何时作罢 浪迹天涯心倦怠哪处归依\n",
|
|||
|
"101376 特地显英灵化被逢人歌泽渥 配天昭厚德恩深无处不波恬\n",
|
|||
|
"262048 温暖鹅城展翅奋飞中国梦 祥和蛇岁铺春欢庆小康年\n",
|
|||
|
"415192 百业一支歌歌伴和风谐雨唱 九江千古梦梦同朗月艳阳圆\n",
|
|||
|
"\n",
|
|||
|
"[5959 rows x 2 columns]\n",
|
|||
|
" in out\n",
|
|||
|
"274864 林霭渐浓迷古寺 尘烟已远隐青山\n",
|
|||
|
"222320 自古青天匡正义 而今华夏振雄风\n",
|
|||
|
"100260 真心请客就该一五一五 假意为情何必我开我开\n",
|
|||
|
"435928 爱本有心今不见 人如无欲意何求\n",
|
|||
|
"446991 欲抹闲愁实不易 谁将片语问何求\n",
|
|||
|
"... ... ...\n",
|
|||
|
"213030 万象随缘观自在 一心发愿待君归\n",
|
|||
|
"299155 春联妙句动心魄 小院雅风入彩光\n",
|
|||
|
"643294 梅亭吹雪横霜笛 松麓邀云放月筝\n",
|
|||
|
"628861 红似桃花白似雪 绿如李叶亮如霜\n",
|
|||
|
"566605 数字双音分两用 联文对句限孤平\n",
|
|||
|
"\n",
|
|||
|
"[1490 rows x 2 columns]\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"male, duze = train_test_split(fixed_couplets,test_size=0.99,random_state=42)\n",
|
|||
|
"treningowe, testowe = train_test_split(male,test_size=0.2,random_state=42)\n",
|
|||
|
"print(treningowe)\n",
|
|||
|
"print(testowe)"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "j_BGuqGizjRU"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Przywrócenie numeracji od 0."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 66,
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "B5Q62xM6zjRU",
|
|||
|
"outputId": "9ea85a50-8583-48ae-e1af-a7221e5032e2"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
" in out\n",
|
|||
|
"0 宇高炎暑净 秋爽飒风来\n",
|
|||
|
"1 忧乐关天下 安危系一身\n",
|
|||
|
"2 一盏相思量寂寞 三分惆怅兑凄凉\n",
|
|||
|
"3 孝驻锦绣城喜吕梁歌飞春融三晋千秋画 义圆和谐梦看汾河景瑞水起九州万卷诗\n",
|
|||
|
"4 春临八桂海豚舞 福满九州彩凤飞\n",
|
|||
|
"... ... ...\n",
|
|||
|
"5954 创中华古老文明当同日月齐辉功垂万代 启黎庶鸿蒙草昧是与山河并寿德颂千秋\n",
|
|||
|
"5955 纠缠海角指相思何时作罢 浪迹天涯心倦怠哪处归依\n",
|
|||
|
"5956 特地显英灵化被逢人歌泽渥 配天昭厚德恩深无处不波恬\n",
|
|||
|
"5957 温暖鹅城展翅奋飞中国梦 祥和蛇岁铺春欢庆小康年\n",
|
|||
|
"5958 百业一支歌歌伴和风谐雨唱 九江千古梦梦同朗月艳阳圆\n",
|
|||
|
"\n",
|
|||
|
"[5959 rows x 2 columns]\n",
|
|||
|
" in out\n",
|
|||
|
"0 林霭渐浓迷古寺 尘烟已远隐青山\n",
|
|||
|
"1 自古青天匡正义 而今华夏振雄风\n",
|
|||
|
"2 真心请客就该一五一五 假意为情何必我开我开\n",
|
|||
|
"3 爱本有心今不见 人如无欲意何求\n",
|
|||
|
"4 欲抹闲愁实不易 谁将片语问何求\n",
|
|||
|
"... ... ...\n",
|
|||
|
"1485 万象随缘观自在 一心发愿待君归\n",
|
|||
|
"1486 春联妙句动心魄 小院雅风入彩光\n",
|
|||
|
"1487 梅亭吹雪横霜笛 松麓邀云放月筝\n",
|
|||
|
"1488 红似桃花白似雪 绿如李叶亮如霜\n",
|
|||
|
"1489 数字双音分两用 联文对句限孤平\n",
|
|||
|
"\n",
|
|||
|
"[1490 rows x 2 columns]\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"treningowe = treningowe.reset_index(drop=True)\n",
|
|||
|
"testowe = testowe.reset_index(drop=True)\n",
|
|||
|
"print(treningowe)\n",
|
|||
|
"print(testowe)"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "6H7qKXA8zjRU"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Pakiet *pypinyin* przewiduje wymowę pinyin dobrze bez potrzeby używania pakietu *jieba*."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 67,
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "eY1Z9DorzjRV",
|
|||
|
"outputId": "04207fb7-c773-4863-9b4c-c5ed07e06a07"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"春临八桂海豚舞\n",
|
|||
|
"[['chun1'], ['lin2'], ['ba1'], ['gui4'], ['hai3'], ['tun2'], ['wu3']]\n",
|
|||
|
"['chun1', 'lin2', 'ba1', 'gui4', 'hai3', 'tun2', 'wu3']\n",
|
|||
|
"['春临', '八桂', '海豚', '舞']\n",
|
|||
|
"[['chun1'], ['lin2'], ['ba1'], ['gui4'], ['hai3'], ['tun2'], ['wu3']]\n",
|
|||
|
"['chun1', 'lin2', 'ba1', 'gui4', 'hai3', 'tun2', 'wu3']\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"from pypinyin import pinyin, lazy_pinyin, Style\n",
|
|||
|
"\n",
|
|||
|
"zdanie = treningowe[\"in\"][4]\n",
|
|||
|
"print(zdanie)\n",
|
|||
|
"print(pinyin(zdanie, style=Style.TONE3, neutral_tone_with_five=True))\n",
|
|||
|
"print(lazy_pinyin(zdanie, style=Style.TONE3, neutral_tone_with_five=True))\n",
|
|||
|
"\n",
|
|||
|
"slowa = list(jieba.cut(zdanie))\n",
|
|||
|
"print(slowa)\n",
|
|||
|
"print(pinyin(slowa, style=Style.TONE3, neutral_tone_with_five=True))\n",
|
|||
|
"print(lazy_pinyin(slowa, style=Style.TONE3, neutral_tone_with_five=True))"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "RjQVSpemzjRV"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"## Podział wymowy pinyin na początki (initials), końcówki (finals) i tony.\n",
|
|||
|
"### Zamina w liczby przy pomocy słownika."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 68,
|
|||
|
"metadata": {
|
|||
|
"id": "jIaoHEofzjRV"
|
|||
|
},
|
|||
|
"outputs": [],
|
|||
|
"source": [
|
|||
|
"from pypinyin.contrib.tone_convert import to_finals, to_initials\n",
|
|||
|
"# 声母表\n",
|
|||
|
"_INITIALS=['b','p','m','f','d','t','n','l','g','k','h','j','q','x','zh','ch','sh','r','z','c','s',]\n",
|
|||
|
"# 声母表,把 y,w 也当作声母\n",
|
|||
|
"_INITIALS_NOT_STRICT=_INITIALS+['y','w']\n",
|
|||
|
"# 韵母表\n",
|
|||
|
"_FINALS=['i','u','ü','a','ia','ua','o','uo','e','ie','üe','ai','uai','ei','uei','ao','iao','ou','iou','an','ian','uan','üan','en','in','uen','ün','ang','iang','uang','eng','ing','ueng','ong','iong','er','ê',]\n",
|
|||
|
"\n",
|
|||
|
"slownik_initials = {}\n",
|
|||
|
"licznik = 1\n",
|
|||
|
"for indeks_wersu_pierwszego in _INITIALS+[\"\"]:\n",
|
|||
|
" slownik_initials[indeks_wersu_pierwszego] = licznik\n",
|
|||
|
" licznik+=1\n",
|
|||
|
"\n",
|
|||
|
"slownik_finals = {}\n",
|
|||
|
"licznik = 1\n",
|
|||
|
"for indeks_wersu_pierwszego in _FINALS+[\"\"]:\n",
|
|||
|
" slownik_finals[indeks_wersu_pierwszego] = licznik\n",
|
|||
|
" licznik+=1\n",
|
|||
|
"\n",
|
|||
|
"def poczatek_koniec_ton(zapis_pinyin_3):\n",
|
|||
|
" poczatek = slownik_initials[to_initials(zapis_pinyin_3)]\n",
|
|||
|
" koniec = slownik_finals[to_finals(zapis_pinyin_3).replace('v', 'ü')]\n",
|
|||
|
" ton = int(zapis_pinyin_3[-1])\n",
|
|||
|
" return poczatek, koniec, ton\n",
|
|||
|
"\n",
|
|||
|
"def wymowy_i_tony_zdania(zdanie):\n",
|
|||
|
" zapis_pinyin_3_zdania = lazy_pinyin(zdanie, style=Style.TONE3, neutral_tone_with_five=True)\n",
|
|||
|
" poczatki = []\n",
|
|||
|
" konce =[]\n",
|
|||
|
" tony = []\n",
|
|||
|
" # print(zdanie, zapis_pinyin_3_zdania)\n",
|
|||
|
" for zp3 in zapis_pinyin_3_zdania:\n",
|
|||
|
" p,k,t = poczatek_koniec_ton(zp3)\n",
|
|||
|
" poczatki.append(p)\n",
|
|||
|
" konce.append(k)\n",
|
|||
|
" tony.append(t)\n",
|
|||
|
" return poczatki, konce, tony\n",
|
|||
|
"\n",
|
|||
|
"def dopasuj_dlugosc_wektora(wektor, dlugosc_wektora):\n",
|
|||
|
" if len(wektor)>dlugosc_wektora:\n",
|
|||
|
" wynik = wektor[:dlugosc_wektora]\n",
|
|||
|
" else:\n",
|
|||
|
" wynik = numpy.pad(wektor,(0,dlugosc_wektora-len(wektor)), mode='constant', constant_values=0)\n",
|
|||
|
" return wynik"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 69,
|
|||
|
"metadata": {
|
|||
|
"id": "Biy31abdzjRV"
|
|||
|
},
|
|||
|
"outputs": [],
|
|||
|
"source": [
|
|||
|
"def poczatki_konce_tony_dla_zdan(zdania, liczba_wejscia):\n",
|
|||
|
" poczatki_wyn = []\n",
|
|||
|
" konce_wyn = []\n",
|
|||
|
" tony_wyn = []\n",
|
|||
|
"\n",
|
|||
|
" for zdanie in zdania:\n",
|
|||
|
" poczatki, konce, tony = wymowy_i_tony_zdania(zdanie)\n",
|
|||
|
"\n",
|
|||
|
" poczatki = dopasuj_dlugosc_wektora(poczatki, liczba_wejscia)\n",
|
|||
|
" konce = dopasuj_dlugosc_wektora(konce, liczba_wejscia)\n",
|
|||
|
" tony = dopasuj_dlugosc_wektora(tony, liczba_wejscia)\n",
|
|||
|
"\n",
|
|||
|
" poczatki_wyn.append(poczatki)\n",
|
|||
|
" konce_wyn.append(konce)\n",
|
|||
|
" tony_wyn.append(tony)\n",
|
|||
|
"\n",
|
|||
|
" return poczatki_wyn, konce_wyn, tony_wyn"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "y_Vzh-kxzjRW"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Początki, końcówki i tony wierszy treningowych."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 70,
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "4qSU-ojazjRW",
|
|||
|
"outputId": "aa8b7b6e-38cc-4950-dbe1-ab4dfca04436"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"5959\n",
|
|||
|
"宇高炎暑净\n",
|
|||
|
"秋爽飒风来\n",
|
|||
|
"5959\n",
|
|||
|
"[22 9 22 17 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
|
|||
|
" 0 0 0 0 0 0 0 0 0 0 0]\n",
|
|||
|
"[ 3 16 21 2 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
|
|||
|
" 0 0 0 0 0 0 0 0 0 0 0]\n",
|
|||
|
"[3 1 2 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
|
|||
|
"[13 17 21 4 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
|
|||
|
" 0 0 0 0 0 0 0 0 0 0 0]\n",
|
|||
|
"[19 30 4 31 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
|
|||
|
" 0 0 0 0 0 0 0 0 0 0 0]\n",
|
|||
|
"[1 3 4 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"print(len(treningowe[\"in\"]))\n",
|
|||
|
"print(treningowe[\"in\"][0])\n",
|
|||
|
"print(treningowe[\"out\"][0])\n",
|
|||
|
"\n",
|
|||
|
"liczba_wejscia = 35\n",
|
|||
|
"\n",
|
|||
|
"poczatki_treningowe_in, konce_treningowe_in, tony_treningowe_in = poczatki_konce_tony_dla_zdan(treningowe[\"in\"], liczba_wejscia)\n",
|
|||
|
"poczatki_treningowe_out, konce_treningowe_out, tony_treningowe_out = poczatki_konce_tony_dla_zdan(treningowe[\"out\"], liczba_wejscia)\n",
|
|||
|
"\n",
|
|||
|
"print(len(poczatki_treningowe_in))\n",
|
|||
|
"print(poczatki_treningowe_in[0])\n",
|
|||
|
"print(konce_treningowe_in[0])\n",
|
|||
|
"print(tony_treningowe_in[0])\n",
|
|||
|
"print(poczatki_treningowe_out[0])\n",
|
|||
|
"print(konce_treningowe_out[0])\n",
|
|||
|
"print(tony_treningowe_out[0])"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "e5hzBe1ZzjRW"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"## Zanurzenia BAAI wierszy treningowych. https://huggingface.co/BAAI/bge-small-zh-v1.5"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 71,
|
|||
|
"metadata": {
|
|||
|
"id": "pZLwcHqbzjRW"
|
|||
|
},
|
|||
|
"outputs": [],
|
|||
|
"source": [
|
|||
|
"# Load model from HuggingFace Hub\n",
|
|||
|
"tokenizer = AutoTokenizer.from_pretrained(\"BAAI/bge-small-zh-v1.5\")\n",
|
|||
|
"model = AutoModel.from_pretrained(\"BAAI/bge-small-zh-v1.5\")\n",
|
|||
|
"model.eval()\n",
|
|||
|
"\n",
|
|||
|
"def zanurzenia_zdan(lista_zdan):\n",
|
|||
|
" # Sentences we want sentence embeddings for\n",
|
|||
|
" #sentences = [\"样例数据-1样例数据\", \"样例数据-2样例数据\"]\n",
|
|||
|
" sentences = lista_zdan\n",
|
|||
|
"\n",
|
|||
|
" # Tokenize sentences\n",
|
|||
|
" encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')\n",
|
|||
|
" # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)\n",
|
|||
|
" # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')\n",
|
|||
|
"\n",
|
|||
|
" # Compute token embeddings\n",
|
|||
|
" with torch.no_grad():\n",
|
|||
|
" model_output = model(**encoded_input)\n",
|
|||
|
" # Perform pooling. In this case, cls pooling.\n",
|
|||
|
" sentence_embeddings = model_output[0][:, 0]\n",
|
|||
|
" # normalize embeddings\n",
|
|||
|
" sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)\n",
|
|||
|
" # print(\"Sentence embeddings shape:\", sentence_embeddings.shape)\n",
|
|||
|
" # print(\"Sentence embeddings:\", sentence_embeddings)\n",
|
|||
|
"\n",
|
|||
|
" return sentence_embeddings\n",
|
|||
|
"\n",
|
|||
|
"def zanurzenie_zdania(zdanie):\n",
|
|||
|
" # Tokenize sentences\n",
|
|||
|
" encoded_input = tokenizer(zdanie, padding=True, truncation=True, return_tensors='pt')\n",
|
|||
|
" # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)\n",
|
|||
|
" # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')\n",
|
|||
|
"\n",
|
|||
|
" # Compute token embeddings\n",
|
|||
|
" with torch.no_grad():\n",
|
|||
|
" model_output = model(**encoded_input)\n",
|
|||
|
" # Perform pooling. In this case, cls pooling.\n",
|
|||
|
" sentence_embedding = model_output[0][:, 0]\n",
|
|||
|
" # normalize embeddings\n",
|
|||
|
" sentence_embedding = torch.nn.functional.normalize(sentence_embedding, p=2, dim=1)\n",
|
|||
|
"\n",
|
|||
|
" return sentence_embedding"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 72,
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "HEDMOtVHzjRX",
|
|||
|
"outputId": "6c175183-20a1-46bf-dd3c-00f7e85b86ec"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"5959\n",
|
|||
|
"宇高炎暑净\n",
|
|||
|
"秋爽飒风来\n",
|
|||
|
"torch.Size([5959, 512])\n",
|
|||
|
"tensor([-3.1409e-02, 4.2919e-02, -1.4236e-02, 6.2288e-02, -3.2497e-02,\n",
|
|||
|
" -5.3290e-02, 4.7686e-02, 7.7745e-02, -2.0447e-02, 2.8347e-02,\n",
|
|||
|
" 1.3510e-02, -2.3332e-01, 8.9271e-04, 4.0544e-02, 9.2784e-05,\n",
|
|||
|
" -1.1740e-02, -1.6238e-02, -4.7785e-02, -8.7547e-02, 6.0501e-02,\n",
|
|||
|
" -1.7588e-02, -4.3948e-03, -3.1034e-02, -8.8176e-03, 3.8507e-02,\n",
|
|||
|
" -5.6918e-02, 6.8194e-02, 7.5235e-03, 6.6778e-03, 2.0831e-02,\n",
|
|||
|
" 9.5349e-04, 4.5033e-02, -9.4392e-03, -3.2470e-02, -3.5810e-02,\n",
|
|||
|
" -2.1215e-02, -2.9089e-02, 3.8043e-02, -2.6267e-02, 5.2310e-02,\n",
|
|||
|
" -6.3633e-02, 4.2117e-02, 1.7834e-02, -5.5019e-02, -7.9315e-02,\n",
|
|||
|
" 8.2654e-04, -1.0802e-02, 1.8213e-02, 6.5130e-02, -7.6177e-03,\n",
|
|||
|
" -3.8167e-02, 5.3484e-02, 3.5490e-02, 2.7366e-02, -3.0560e-02,\n",
|
|||
|
" -7.8364e-02, -3.3920e-02, 7.5826e-03, -1.6268e-02, 1.9344e-02,\n",
|
|||
|
" 3.4404e-02, 1.1855e-02, -3.5319e-02, -1.1730e-02, 6.7642e-02,\n",
|
|||
|
" 3.7650e-04, 1.9837e-02, 8.3773e-03, -1.1069e-02, 3.2787e-02,\n",
|
|||
|
" 4.2034e-03, 3.5917e-03, 5.5923e-02, -5.6078e-02, -3.9402e-02,\n",
|
|||
|
" -5.3479e-02, 1.2802e-03, -3.0281e-02, -5.1191e-02, -1.7256e-02,\n",
|
|||
|
" 1.4092e-02, -2.8433e-02, -5.4772e-02, -4.3614e-02, -1.3096e-02,\n",
|
|||
|
" 1.8400e-02, -1.1333e-01, -4.3593e-02, 2.8702e-02, 5.6857e-04,\n",
|
|||
|
" -9.5228e-03, -9.2662e-03, 1.4085e-02, -1.0477e-02, -7.0193e-02,\n",
|
|||
|
" 6.9635e-02, -2.4111e-02, 1.2565e-02, -6.6401e-02, -4.1899e-02,\n",
|
|||
|
" -2.5085e-02, -6.3970e-02, 5.7718e-02, 6.8888e-02, -6.1210e-02,\n",
|
|||
|
" 6.5007e-02, -8.2084e-02, -5.9957e-02, 6.8816e-03, -1.0067e-03,\n",
|
|||
|
" 3.7481e-02, 2.3379e-02, 2.7860e-02, 4.7394e-02, -3.3720e-02,\n",
|
|||
|
" -6.1802e-02, 9.0069e-02, 1.4320e-02, -6.6455e-02, -8.1411e-02,\n",
|
|||
|
" -4.2551e-02, -4.0180e-02, 7.1318e-02, 4.7259e-02, -6.1807e-03,\n",
|
|||
|
" 1.6717e-02, -2.7057e-02, -1.9109e-02, 5.8335e-02, 4.2307e-02,\n",
|
|||
|
" 3.6037e-03, 8.2558e-03, -2.4797e-03, -1.4135e-02, -2.2754e-02,\n",
|
|||
|
" -2.2781e-03, -5.8836e-03, -1.1159e-01, 2.3304e-03, -1.3209e-02,\n",
|
|||
|
" -1.2044e-03, -2.6060e-02, -1.6546e-02, -1.3189e-02, 7.0787e-02,\n",
|
|||
|
" -2.1846e-02, -3.3586e-02, -2.8873e-02, 3.0573e-02, -5.1569e-02,\n",
|
|||
|
" 7.2466e-03, 5.6971e-02, 3.2711e-02, -1.2560e-02, -5.2461e-02,\n",
|
|||
|
" -5.3417e-03, -4.4110e-03, -1.7080e-02, -1.5891e-02, 5.0826e-02,\n",
|
|||
|
" 5.6342e-02, -1.2345e-03, 2.6094e-02, 3.0109e-02, -1.6446e-02,\n",
|
|||
|
" -2.2257e-02, 3.8077e-03, 8.1443e-02, -4.4790e-02, 7.7875e-02,\n",
|
|||
|
" 5.6147e-02, -1.2718e-02, -4.7217e-02, 3.8158e-02, -4.8242e-03,\n",
|
|||
|
" -3.3682e-02, -3.7652e-02, 5.8250e-02, 1.5820e-02, 3.1382e-02,\n",
|
|||
|
" 1.3865e-02, 7.9274e-02, -2.9852e-02, 5.9575e-02, -9.7192e-03,\n",
|
|||
|
" -1.3883e-02, 1.3156e-02, -2.2232e-02, -2.1665e-02, -3.9232e-02,\n",
|
|||
|
" -9.3653e-03, -6.2756e-03, 3.1561e-02, 3.8607e-02, 8.6990e-03,\n",
|
|||
|
" 6.4413e-02, -7.7960e-02, -4.7945e-02, -1.4091e-01, 5.0533e-02,\n",
|
|||
|
" 2.0320e-03, -5.1708e-02, -1.9163e-03, 2.4024e-02, -2.0240e-02,\n",
|
|||
|
" 2.1377e-02, 3.5398e-03, 3.7191e-02, -3.6258e-02, -5.4974e-02,\n",
|
|||
|
" -1.6857e-02, 6.0301e-02, 1.4563e-02, -4.0892e-02, 1.4841e-01,\n",
|
|||
|
" 2.6193e-02, -9.0119e-04, 8.5365e-03, 1.2007e-02, -1.0382e-01,\n",
|
|||
|
" 3.3631e-02, 7.6653e-02, 2.4468e-02, -7.0872e-03, -1.8002e-02,\n",
|
|||
|
" -8.5119e-03, -1.4168e-02, 1.5942e-02, -2.9196e-02, -6.4297e-04,\n",
|
|||
|
" 1.6337e-02, -1.9513e-02, 1.3898e-02, 3.9867e-03, 2.6298e-02,\n",
|
|||
|
" 4.6379e-02, -7.1418e-02, 1.0134e-02, 5.1168e-02, -4.9732e-02,\n",
|
|||
|
" -5.5967e-02, -7.8217e-03, 2.8585e-02, -8.7352e-03, -9.9658e-05,\n",
|
|||
|
" 1.2468e-02, -7.3671e-02, -2.2079e-03, 5.4546e-03, 3.5459e-02,\n",
|
|||
|
" -3.2250e-02, 9.3758e-02, -1.1456e-02, -3.1892e-02, -4.1353e-02,\n",
|
|||
|
" 3.3040e-02, -3.7227e-03, 2.6740e-02, -6.4840e-02, 4.1143e-02,\n",
|
|||
|
" -1.8554e-02, 1.5613e-02, 5.0357e-03, 1.1793e-02, 1.3087e-02,\n",
|
|||
|
" -4.2158e-02, -1.5489e-02, 2.7196e-02, -3.9413e-04, 1.1546e-02,\n",
|
|||
|
" -3.9742e-02, 7.9554e-03, 5.7563e-02, -5.5298e-02, 1.0457e-02,\n",
|
|||
|
" 5.1986e-02, -3.2875e-03, 2.1230e-02, 6.3298e-03, 7.0061e-03,\n",
|
|||
|
" 1.7268e-02, 7.4763e-02, -8.9870e-02, 1.9039e-02, -5.3741e-03,\n",
|
|||
|
" -4.5542e-02, -1.4080e-01, 3.6304e-02, -1.4179e-02, -2.1746e-02,\n",
|
|||
|
" 1.3878e-02, -8.1540e-02, 4.5647e-02, 2.8653e-02, -1.1617e-02,\n",
|
|||
|
" 2.7410e-02, -3.9985e-02, -6.1613e-03, 6.7774e-02, 1.1290e-02,\n",
|
|||
|
" 4.6115e-02, 2.3358e-02, -2.1498e-02, -4.1548e-02, 1.3849e-02,\n",
|
|||
|
" 1.2356e-02, 4.1165e-03, 4.5328e-02, -3.5151e-02, -2.3484e-03,\n",
|
|||
|
" 1.2952e-03, -2.0535e-02, -3.0788e-02, -4.7044e-02, 1.5876e-02,\n",
|
|||
|
" -1.1296e-03, -1.8713e-02, 1.8543e-02, 5.3209e-02, 2.7803e-02,\n",
|
|||
|
" 1.1028e-03, 2.9207e-02, -3.6119e-02, -1.6165e-02, 1.7555e-02,\n",
|
|||
|
" -8.2125e-03, 6.3445e-03, -4.5027e-02, -4.0817e-02, 4.5773e-02,\n",
|
|||
|
" -2.2641e-02, 5.2889e-02, 1.4512e-02, -1.9522e-02, -5.6481e-02,\n",
|
|||
|
" -1.1060e-02, -4.3722e-02, 1.4095e-02, 2.4259e-02, 6.0377e-02,\n",
|
|||
|
" -7.2628e-02, 3.9760e-02, -8.1585e-02, -9.1420e-03, 1.8809e-03,\n",
|
|||
|
" 1.0487e-01, -4.9327e-02, -5.5549e-03, 4.6258e-02, 1.6701e-02,\n",
|
|||
|
" -1.6163e-02, 2.6286e-02, -2.7700e-02, 8.0984e-03, 2.4454e-02,\n",
|
|||
|
" 4.4797e-02, 3.4455e-02, -6.0768e-02, -2.5864e-02, 1.0166e-03,\n",
|
|||
|
" 5.3068e-03, 4.2425e-03, -5.4753e-02, -3.1478e-02, 3.7924e-02,\n",
|
|||
|
" 1.0266e-03, 8.4248e-03, 4.5199e-02, 1.5580e-02, -5.6708e-03,\n",
|
|||
|
" 3.6769e-02, -5.3641e-02, 4.1779e-02, -3.2060e-02, -2.6757e-02,\n",
|
|||
|
" -4.7505e-02, -8.2457e-02, 7.2944e-02, 1.5763e-02, 1.6309e-02,\n",
|
|||
|
" 7.5724e-04, 6.4329e-02, 1.9464e-02, -2.7392e-03, 4.2363e-02,\n",
|
|||
|
" -1.0416e-01, -1.6209e-02, 4.5399e-02, 1.0589e-01, -2.4638e-02,\n",
|
|||
|
" 2.6731e-02, 2.6622e-02, 4.0844e-02, 1.0323e-01, 3.3835e-02,\n",
|
|||
|
" -4.2006e-02, 4.2951e-02, 3.1068e-02, -2.4564e-03, -1.2811e-02,\n",
|
|||
|
" -8.4661e-03, 3.9647e-02, -1.1733e-01, 2.6631e-02, -3.1336e-02,\n",
|
|||
|
" -1.0026e-01, -6.3246e-03, -1.8747e-02, -8.8051e-03, -6.3902e-02,\n",
|
|||
|
" 2.0967e-01, 3.4409e-02, -1.6454e-02, 3.0606e-02, -2.1813e-02,\n",
|
|||
|
" 9.1961e-02, 4.4120e-02, -2.1517e-02, -3.4456e-02, -5.8409e-02,\n",
|
|||
|
" 2.7488e-02, 1.9422e-02, -1.2918e-02, -7.2962e-03, 2.8859e-02,\n",
|
|||
|
" -4.2516e-02, -4.2966e-02, -1.9645e-02, -6.4296e-02, -4.0894e-02,\n",
|
|||
|
" -2.8706e-02, -5.6346e-02, 3.4201e-02, -3.9250e-03, 7.5307e-02,\n",
|
|||
|
" 6.5123e-03, -4.7450e-02, -3.1443e-02, -5.0485e-02, 6.2536e-02,\n",
|
|||
|
" -2.6723e-02, 3.9097e-02, 2.5871e-03, 4.8988e-02, -1.2248e-05,\n",
|
|||
|
" 1.8120e-02, -1.4111e-02, -2.9327e-02, 7.4617e-02, 8.2369e-03,\n",
|
|||
|
" -3.3414e-02, -1.0466e-02, -4.4706e-03, -1.3613e-02, -5.4163e-02,\n",
|
|||
|
" -4.4011e-02, -7.4851e-02, -5.5124e-02, 8.7570e-03, 3.8449e-02,\n",
|
|||
|
" -5.1844e-02, 5.9674e-03, 7.5129e-03, 1.0718e-02, 2.1981e-02,\n",
|
|||
|
" 4.4945e-02, -3.4382e-02, -5.1930e-02, 1.5666e-02, -3.3479e-02,\n",
|
|||
|
" -2.9640e-03, -1.2958e-02, -3.5843e-02, -2.9896e-02, 7.1761e-02,\n",
|
|||
|
" -3.2109e-02, 1.1761e-01, 1.1047e-02, -4.7208e-02, -3.3970e-02,\n",
|
|||
|
" 7.1073e-02, -9.1408e-02, 6.3568e-03, -7.5566e-03, -8.2016e-03,\n",
|
|||
|
" -9.3746e-03, 1.5221e-02, 7.5551e-03, -4.2618e-02, 2.9687e-02,\n",
|
|||
|
" 4.7213e-02, -5.6087e-02, -3.5213e-02, -6.5220e-02, 1.8469e-02,\n",
|
|||
|
" 6.4949e-02, -1.9809e-02, -8.2783e-02, -3.2709e-03, -5.1782e-02,\n",
|
|||
|
" -6.3309e-02, 3.6822e-02, -8.6364e-04, -1.7256e-02, 6.0698e-03,\n",
|
|||
|
" -2.0665e-02, 1.7764e-02, 8.8567e-02, -5.4184e-02, -1.4816e-02,\n",
|
|||
|
" 6.1665e-02, 2.7374e-02])\n",
|
|||
|
"tensor([ 2.4096e-02, 7.3348e-02, -8.4988e-03, 2.1168e-02, -5.1689e-02,\n",
|
|||
|
" -3.5376e-04, 4.7075e-02, 2.5451e-02, 3.5129e-02, 4.6819e-02,\n",
|
|||
|
" 6.8763e-02, -2.2109e-01, -2.9449e-02, 5.0597e-02, -2.5865e-02,\n",
|
|||
|
" -9.3008e-03, 2.8629e-02, -6.2801e-02, -6.8237e-02, -5.9068e-02,\n",
|
|||
|
" 2.1109e-02, -3.3667e-02, -3.0538e-02, 1.0535e-01, 3.0778e-02,\n",
|
|||
|
" -2.6921e-02, -4.7817e-03, 2.0352e-02, -6.7792e-02, 6.7208e-02,\n",
|
|||
|
" 1.7218e-02, 1.9034e-02, -5.1180e-02, -1.4875e-02, -1.5020e-02,\n",
|
|||
|
" -1.2319e-02, -6.5349e-02, 4.0683e-02, 5.6421e-02, -2.2507e-02,\n",
|
|||
|
" -2.5330e-02, 4.9632e-02, 5.9727e-02, 9.1537e-03, -2.7953e-02,\n",
|
|||
|
" -4.3726e-02, -3.3593e-02, 1.8592e-02, 1.5352e-03, -9.2273e-03,\n",
|
|||
|
" -6.5650e-02, 3.0612e-02, -6.9992e-02, -2.7435e-02, 2.9220e-02,\n",
|
|||
|
" -3.2722e-02, -3.1333e-02, 1.2232e-02, -6.3038e-02, 6.2572e-04,\n",
|
|||
|
" 2.0118e-02, -4.7327e-02, -4.6759e-02, 1.6298e-02, 2.4694e-02,\n",
|
|||
|
" -1.5708e-02, -1.7262e-02, -1.1750e-02, -3.4596e-03, -5.3582e-02,\n",
|
|||
|
" -8.0472e-02, 5.7651e-02, 3.8062e-02, -7.1649e-02, 4.5374e-02,\n",
|
|||
|
" -7.1557e-02, 1.8123e-02, 3.5019e-02, -8.7280e-02, -5.9952e-03,\n",
|
|||
|
" 1.3746e-02, 1.6378e-02, -4.3599e-02, 1.0333e-02, -1.3245e-02,\n",
|
|||
|
" -3.2981e-02, -6.4206e-02, -2.4593e-02, 3.1208e-02, -9.5114e-03,\n",
|
|||
|
" -5.2171e-02, -4.6604e-02, 5.0359e-02, 4.7381e-02, -7.6541e-03,\n",
|
|||
|
" 1.7540e-02, -7.5362e-03, -1.0370e-03, -2.0973e-02, -5.8539e-02,\n",
|
|||
|
" -4.2109e-03, -7.7784e-02, 4.7974e-02, 1.5605e-02, 1.1676e-02,\n",
|
|||
|
" 5.4789e-02, 2.6982e-02, 2.8896e-02, 1.4084e-02, 3.6774e-02,\n",
|
|||
|
" -5.7120e-02, 8.5216e-02, -1.8359e-02, 1.8367e-02, -5.9878e-02,\n",
|
|||
|
" 2.1155e-02, 3.2800e-03, 1.5960e-02, -1.3590e-01, 6.6871e-02,\n",
|
|||
|
" 5.0083e-03, 4.7189e-03, 9.8846e-02, -4.0727e-02, -1.1970e-01,\n",
|
|||
|
" 4.3001e-03, -3.3519e-02, -1.2028e-02, 5.3046e-02, 6.3472e-02,\n",
|
|||
|
" 8.0517e-03, -1.6034e-02, 1.1180e-02, -2.7315e-02, -1.9381e-02,\n",
|
|||
|
" -2.0683e-02, 3.7952e-03, -7.2708e-02, -3.0257e-02, 7.5861e-03,\n",
|
|||
|
" -3.0704e-02, -7.9766e-03, 9.0976e-03, -6.8949e-02, 9.3395e-02,\n",
|
|||
|
" -5.1396e-02, 4.6734e-02, -1.2085e-03, 2.2747e-02, 4.4702e-02,\n",
|
|||
|
" -1.9269e-02, -3.2044e-02, 4.6390e-02, 5.6546e-02, -3.7156e-02,\n",
|
|||
|
" 3.9877e-02, 1.0895e-02, -1.6061e-02, -6.7260e-02, 1.6562e-02,\n",
|
|||
|
" 1.2008e-03, 3.7859e-02, 3.9005e-02, 3.4202e-02, -1.4327e-02,\n",
|
|||
|
" -8.2659e-02, 1.9792e-02, 1.5776e-03, -6.7330e-02, 4.3296e-02,\n",
|
|||
|
" -4.3103e-02, -8.2537e-03, 3.0699e-02, -1.7245e-02, 5.5340e-02,\n",
|
|||
|
" -7.3155e-03, 2.0148e-02, -2.6217e-02, -1.6741e-03, 7.1648e-02,\n",
|
|||
|
" 2.5549e-02, 2.2865e-02, -2.0414e-03, -1.6362e-02, 4.6387e-03,\n",
|
|||
|
" 2.8256e-02, 2.3293e-02, -2.2062e-02, -9.2340e-03, -1.1985e-02,\n",
|
|||
|
" 8.0533e-04, -2.3884e-02, 5.9400e-02, -1.1038e-02, 4.8180e-03,\n",
|
|||
|
" 3.5944e-02, -6.4729e-02, -1.1301e-02, -5.6865e-02, 1.8658e-02,\n",
|
|||
|
" -1.4537e-02, -2.3870e-02, 1.8639e-02, 6.1247e-02, 1.8494e-03,\n",
|
|||
|
" 3.9511e-03, -1.1623e-02, 2.7783e-02, -9.0809e-02, -4.3361e-02,\n",
|
|||
|
" -4.4524e-02, 9.5100e-03, 8.1598e-03, -5.9092e-02, 2.2854e-02,\n",
|
|||
|
" 1.0801e-02, 5.5640e-02, -7.4158e-03, -3.0120e-02, -4.7106e-02,\n",
|
|||
|
" -2.8703e-02, 6.2336e-02, -8.6966e-02, -8.8282e-02, -2.9747e-02,\n",
|
|||
|
" -2.8669e-02, 2.8053e-02, -3.0225e-02, -2.4561e-02, -1.2942e-02,\n",
|
|||
|
" -4.3129e-02, -5.1436e-02, 3.2625e-02, -4.6949e-02, -1.2704e-02,\n",
|
|||
|
" 2.7554e-02, 1.4629e-02, 3.8203e-02, -8.7354e-02, -2.7942e-02,\n",
|
|||
|
" -4.2217e-02, 4.5440e-02, -1.1199e-02, 1.5859e-02, -5.7629e-02,\n",
|
|||
|
" -3.4809e-02, -5.4919e-02, 1.9037e-02, 1.0293e-02, 6.9702e-03,\n",
|
|||
|
" -3.0121e-02, 7.6800e-02, -1.9755e-02, -1.2176e-01, -4.2284e-02,\n",
|
|||
|
" -5.6440e-02, -3.4314e-02, -3.0538e-02, -5.3078e-02, -2.0438e-02,\n",
|
|||
|
" -2.7687e-03, 1.5685e-02, 8.3713e-03, 1.4941e-02, 2.8835e-02,\n",
|
|||
|
" -1.5773e-02, -2.2957e-02, 3.4821e-02, 8.3100e-03, -3.6987e-02,\n",
|
|||
|
" 1.0159e-03, 3.6687e-02, 1.5403e-02, -7.7245e-02, 1.1903e-02,\n",
|
|||
|
" 3.9656e-02, 5.8933e-02, 1.1769e-03, -7.7724e-03, 1.0608e-01,\n",
|
|||
|
" -1.3163e-02, -6.9340e-03, -2.9777e-02, 3.8290e-02, 2.5452e-02,\n",
|
|||
|
" -4.4490e-02, -1.2190e-01, -9.1041e-03, 8.4519e-03, -1.0265e-03,\n",
|
|||
|
" 3.0511e-02, -4.8933e-02, 3.1984e-03, 1.9107e-02, -1.9031e-02,\n",
|
|||
|
" -2.7986e-02, 2.8155e-02, -3.2111e-02, 5.3439e-02, -6.6016e-02,\n",
|
|||
|
" 2.2510e-02, -2.5893e-02, 2.5647e-02, 6.2114e-02, 3.6392e-03,\n",
|
|||
|
" 2.1521e-02, 1.0638e-03, 4.0820e-02, -2.1784e-02, 2.3471e-02,\n",
|
|||
|
" 6.5689e-03, 4.1211e-02, 2.2548e-02, -6.9367e-02, 7.2007e-02,\n",
|
|||
|
" -2.3249e-02, 9.7457e-03, 5.0128e-03, 1.9682e-03, 1.1460e-02,\n",
|
|||
|
" -1.1636e-03, 1.2196e-02, -8.2566e-03, -1.2993e-02, 4.0637e-02,\n",
|
|||
|
" -1.2862e-02, -9.3435e-03, 3.5840e-02, -1.3115e-02, 6.7564e-02,\n",
|
|||
|
" -1.3449e-02, 8.3304e-02, 1.3780e-02, -6.5205e-03, 2.1614e-02,\n",
|
|||
|
" -4.6509e-02, -2.3400e-02, -1.1252e-02, -2.1349e-03, 9.9767e-02,\n",
|
|||
|
" 5.9413e-02, -6.5736e-03, -4.4302e-02, 1.0448e-02, -1.8580e-02,\n",
|
|||
|
" 6.8594e-02, -1.4184e-02, -7.0092e-02, -3.2865e-02, 1.1723e-02,\n",
|
|||
|
" 9.1901e-03, -1.5251e-02, -1.4926e-02, -3.3470e-02, -3.6489e-03,\n",
|
|||
|
" -3.8432e-02, 1.9594e-02, 2.5313e-02, -4.9300e-02, 6.5015e-02,\n",
|
|||
|
" -3.0438e-02, -9.3662e-03, 3.4233e-02, -7.8762e-02, -6.7159e-03,\n",
|
|||
|
" 3.1354e-02, -2.0526e-02, 5.4133e-03, 1.1246e-02, 2.1658e-02,\n",
|
|||
|
" -1.0054e-02, 2.1285e-02, 1.1186e-01, -3.7673e-02, 2.4505e-02,\n",
|
|||
|
" -2.0750e-02, -3.7844e-02, -2.8911e-02, 9.4496e-03, 1.4896e-02,\n",
|
|||
|
" -3.0971e-02, -1.8133e-02, -4.7326e-02, -2.8264e-02, 4.9661e-02,\n",
|
|||
|
" -1.8136e-02, -2.1942e-02, -2.6936e-02, 2.0541e-02, 4.2219e-03,\n",
|
|||
|
" 6.6803e-02, -6.7906e-02, -3.7795e-02, -2.2262e-02, 3.3751e-02,\n",
|
|||
|
" 1.1071e-02, 4.1053e-02, -6.2190e-02, 4.3035e-03, -3.6697e-02,\n",
|
|||
|
" -2.4697e-03, 3.2390e-02, -6.7590e-02, 3.7872e-02, 3.5083e-02,\n",
|
|||
|
" -4.1133e-02, 1.5301e-02, -9.9107e-03, -5.2390e-02, 6.0837e-02,\n",
|
|||
|
" 2.2806e-01, -7.3393e-02, 2.9662e-02, -6.6508e-02, -1.7553e-02,\n",
|
|||
|
" 7.5196e-02, -3.6798e-02, 4.5125e-03, 3.4317e-02, -5.6979e-02,\n",
|
|||
|
" 5.9627e-02, 7.8637e-02, -6.2195e-02, 4.5452e-02, -2.8716e-03,\n",
|
|||
|
" 8.0530e-02, -1.8484e-02, 2.2444e-02, -2.6805e-02, -2.2107e-02,\n",
|
|||
|
" -2.1742e-02, -3.0206e-02, 7.3662e-02, 4.2316e-02, 4.5892e-02,\n",
|
|||
|
" -2.8320e-02, 6.5208e-02, -4.3190e-02, -5.5195e-02, -7.3266e-02,\n",
|
|||
|
" -1.6800e-03, 4.9327e-02, 3.7236e-02, 1.3723e-02, 2.8840e-02,\n",
|
|||
|
" 9.9783e-03, -4.3477e-02, 2.6408e-02, -5.9908e-03, 3.1495e-02,\n",
|
|||
|
" -1.3816e-03, 1.8268e-02, -2.0290e-02, -7.3615e-02, -4.2263e-02,\n",
|
|||
|
" 3.5367e-02, -4.4292e-02, -7.9611e-02, 7.9907e-02, 4.5494e-02,\n",
|
|||
|
" -3.2248e-02, 1.6629e-02, -7.5351e-03, 2.1802e-02, -3.3684e-02,\n",
|
|||
|
" -1.4436e-02, 2.1520e-02, -6.3879e-02, 1.0100e-02, -2.5601e-05,\n",
|
|||
|
" -1.9271e-02, 4.7454e-02, -2.4488e-02, 6.5203e-03, 5.9140e-02,\n",
|
|||
|
" 3.7843e-02, 3.8729e-02, 3.5719e-02, 6.4549e-02, 3.9975e-02,\n",
|
|||
|
" -7.7090e-03, -3.8202e-02, -4.2739e-02, 6.9333e-02, -3.2327e-02,\n",
|
|||
|
" 1.3822e-01, 7.5231e-03, 1.8590e-02, -2.8336e-02, 7.5397e-02,\n",
|
|||
|
" -8.1537e-03, -7.2928e-02, -6.6228e-02, 1.4838e-02, -2.3286e-02,\n",
|
|||
|
" 4.9019e-02, 1.8467e-02, -6.7986e-02, -4.8970e-02, -2.9831e-02,\n",
|
|||
|
" 4.9185e-02, 3.9403e-03, 6.8458e-02, 4.9250e-02, -9.2371e-02,\n",
|
|||
|
" -1.7414e-02, 3.7454e-02, 4.5524e-02, -4.9280e-02, 5.0603e-02,\n",
|
|||
|
" 5.4588e-03, -5.6567e-02])\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"treningowe_in_lista = treningowe[\"in\"].tolist()\n",
|
|||
|
"treningowe_out_lista = treningowe[\"out\"].tolist()\n",
|
|||
|
"\n",
|
|||
|
"print(len(treningowe_in_lista))\n",
|
|||
|
"print(treningowe_in_lista[0])\n",
|
|||
|
"print(treningowe_out_lista[0])\n",
|
|||
|
"\n",
|
|||
|
"zanurzenia_treningowe_in = zanurzenia_zdan(treningowe_in_lista)\n",
|
|||
|
"zanurzenia_treningowe_out = zanurzenia_zdan(treningowe_out_lista)\n",
|
|||
|
"\n",
|
|||
|
"print(zanurzenia_treningowe_in.shape)\n",
|
|||
|
"print(zanurzenia_treningowe_in[0])\n",
|
|||
|
"print(zanurzenia_treningowe_out[0])"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "qQhQ5P17zjRX"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Tensory - reprezentacje pierwszych wersów wierszy treningowych."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 73,
|
|||
|
"metadata": {
|
|||
|
"id": "pgDpVRShzjRX",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "00aa5db5-f523-4279-fe99-9a394bdfc9bf"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"5959\n",
|
|||
|
"torch.Size([617])\n",
|
|||
|
"tensor([-3.1409e-02, 4.2919e-02, -1.4236e-02, 6.2288e-02, -3.2497e-02,\n",
|
|||
|
" -5.3290e-02, 4.7686e-02, 7.7745e-02, -2.0447e-02, 2.8347e-02,\n",
|
|||
|
" 1.3510e-02, -2.3332e-01, 8.9271e-04, 4.0544e-02, 9.2784e-05,\n",
|
|||
|
" -1.1740e-02, -1.6238e-02, -4.7785e-02, -8.7547e-02, 6.0501e-02,\n",
|
|||
|
" -1.7588e-02, -4.3948e-03, -3.1034e-02, -8.8176e-03, 3.8507e-02,\n",
|
|||
|
" -5.6918e-02, 6.8194e-02, 7.5235e-03, 6.6778e-03, 2.0831e-02,\n",
|
|||
|
" 9.5349e-04, 4.5033e-02, -9.4392e-03, -3.2470e-02, -3.5810e-02,\n",
|
|||
|
" -2.1215e-02, -2.9089e-02, 3.8043e-02, -2.6267e-02, 5.2310e-02,\n",
|
|||
|
" -6.3633e-02, 4.2117e-02, 1.7834e-02, -5.5019e-02, -7.9315e-02,\n",
|
|||
|
" 8.2654e-04, -1.0802e-02, 1.8213e-02, 6.5130e-02, -7.6177e-03,\n",
|
|||
|
" -3.8167e-02, 5.3484e-02, 3.5490e-02, 2.7366e-02, -3.0560e-02,\n",
|
|||
|
" -7.8364e-02, -3.3920e-02, 7.5826e-03, -1.6268e-02, 1.9344e-02,\n",
|
|||
|
" 3.4404e-02, 1.1855e-02, -3.5319e-02, -1.1730e-02, 6.7642e-02,\n",
|
|||
|
" 3.7650e-04, 1.9837e-02, 8.3773e-03, -1.1069e-02, 3.2787e-02,\n",
|
|||
|
" 4.2034e-03, 3.5917e-03, 5.5923e-02, -5.6078e-02, -3.9402e-02,\n",
|
|||
|
" -5.3479e-02, 1.2802e-03, -3.0281e-02, -5.1191e-02, -1.7256e-02,\n",
|
|||
|
" 1.4092e-02, -2.8433e-02, -5.4772e-02, -4.3614e-02, -1.3096e-02,\n",
|
|||
|
" 1.8400e-02, -1.1333e-01, -4.3593e-02, 2.8702e-02, 5.6857e-04,\n",
|
|||
|
" -9.5228e-03, -9.2662e-03, 1.4085e-02, -1.0477e-02, -7.0193e-02,\n",
|
|||
|
" 6.9635e-02, -2.4111e-02, 1.2565e-02, -6.6401e-02, -4.1899e-02,\n",
|
|||
|
" -2.5085e-02, -6.3970e-02, 5.7718e-02, 6.8888e-02, -6.1210e-02,\n",
|
|||
|
" 6.5007e-02, -8.2084e-02, -5.9957e-02, 6.8816e-03, -1.0067e-03,\n",
|
|||
|
" 3.7481e-02, 2.3379e-02, 2.7860e-02, 4.7394e-02, -3.3720e-02,\n",
|
|||
|
" -6.1802e-02, 9.0069e-02, 1.4320e-02, -6.6455e-02, -8.1411e-02,\n",
|
|||
|
" -4.2551e-02, -4.0180e-02, 7.1318e-02, 4.7259e-02, -6.1807e-03,\n",
|
|||
|
" 1.6717e-02, -2.7057e-02, -1.9109e-02, 5.8335e-02, 4.2307e-02,\n",
|
|||
|
" 3.6037e-03, 8.2558e-03, -2.4797e-03, -1.4135e-02, -2.2754e-02,\n",
|
|||
|
" -2.2781e-03, -5.8836e-03, -1.1159e-01, 2.3304e-03, -1.3209e-02,\n",
|
|||
|
" -1.2044e-03, -2.6060e-02, -1.6546e-02, -1.3189e-02, 7.0787e-02,\n",
|
|||
|
" -2.1846e-02, -3.3586e-02, -2.8873e-02, 3.0573e-02, -5.1569e-02,\n",
|
|||
|
" 7.2466e-03, 5.6971e-02, 3.2711e-02, -1.2560e-02, -5.2461e-02,\n",
|
|||
|
" -5.3417e-03, -4.4110e-03, -1.7080e-02, -1.5891e-02, 5.0826e-02,\n",
|
|||
|
" 5.6342e-02, -1.2345e-03, 2.6094e-02, 3.0109e-02, -1.6446e-02,\n",
|
|||
|
" -2.2257e-02, 3.8077e-03, 8.1443e-02, -4.4790e-02, 7.7875e-02,\n",
|
|||
|
" 5.6147e-02, -1.2718e-02, -4.7217e-02, 3.8158e-02, -4.8242e-03,\n",
|
|||
|
" -3.3682e-02, -3.7652e-02, 5.8250e-02, 1.5820e-02, 3.1382e-02,\n",
|
|||
|
" 1.3865e-02, 7.9274e-02, -2.9852e-02, 5.9575e-02, -9.7192e-03,\n",
|
|||
|
" -1.3883e-02, 1.3156e-02, -2.2232e-02, -2.1665e-02, -3.9232e-02,\n",
|
|||
|
" -9.3653e-03, -6.2756e-03, 3.1561e-02, 3.8607e-02, 8.6990e-03,\n",
|
|||
|
" 6.4413e-02, -7.7960e-02, -4.7945e-02, -1.4091e-01, 5.0533e-02,\n",
|
|||
|
" 2.0320e-03, -5.1708e-02, -1.9163e-03, 2.4024e-02, -2.0240e-02,\n",
|
|||
|
" 2.1377e-02, 3.5398e-03, 3.7191e-02, -3.6258e-02, -5.4974e-02,\n",
|
|||
|
" -1.6857e-02, 6.0301e-02, 1.4563e-02, -4.0892e-02, 1.4841e-01,\n",
|
|||
|
" 2.6193e-02, -9.0119e-04, 8.5365e-03, 1.2007e-02, -1.0382e-01,\n",
|
|||
|
" 3.3631e-02, 7.6653e-02, 2.4468e-02, -7.0872e-03, -1.8002e-02,\n",
|
|||
|
" -8.5119e-03, -1.4168e-02, 1.5942e-02, -2.9196e-02, -6.4297e-04,\n",
|
|||
|
" 1.6337e-02, -1.9513e-02, 1.3898e-02, 3.9867e-03, 2.6298e-02,\n",
|
|||
|
" 4.6379e-02, -7.1418e-02, 1.0134e-02, 5.1168e-02, -4.9732e-02,\n",
|
|||
|
" -5.5967e-02, -7.8217e-03, 2.8585e-02, -8.7352e-03, -9.9658e-05,\n",
|
|||
|
" 1.2468e-02, -7.3671e-02, -2.2079e-03, 5.4546e-03, 3.5459e-02,\n",
|
|||
|
" -3.2250e-02, 9.3758e-02, -1.1456e-02, -3.1892e-02, -4.1353e-02,\n",
|
|||
|
" 3.3040e-02, -3.7227e-03, 2.6740e-02, -6.4840e-02, 4.1143e-02,\n",
|
|||
|
" -1.8554e-02, 1.5613e-02, 5.0357e-03, 1.1793e-02, 1.3087e-02,\n",
|
|||
|
" -4.2158e-02, -1.5489e-02, 2.7196e-02, -3.9413e-04, 1.1546e-02,\n",
|
|||
|
" -3.9742e-02, 7.9554e-03, 5.7563e-02, -5.5298e-02, 1.0457e-02,\n",
|
|||
|
" 5.1986e-02, -3.2875e-03, 2.1230e-02, 6.3298e-03, 7.0061e-03,\n",
|
|||
|
" 1.7268e-02, 7.4763e-02, -8.9870e-02, 1.9039e-02, -5.3741e-03,\n",
|
|||
|
" -4.5542e-02, -1.4080e-01, 3.6304e-02, -1.4179e-02, -2.1746e-02,\n",
|
|||
|
" 1.3878e-02, -8.1540e-02, 4.5647e-02, 2.8653e-02, -1.1617e-02,\n",
|
|||
|
" 2.7410e-02, -3.9985e-02, -6.1613e-03, 6.7774e-02, 1.1290e-02,\n",
|
|||
|
" 4.6115e-02, 2.3358e-02, -2.1498e-02, -4.1548e-02, 1.3849e-02,\n",
|
|||
|
" 1.2356e-02, 4.1165e-03, 4.5328e-02, -3.5151e-02, -2.3484e-03,\n",
|
|||
|
" 1.2952e-03, -2.0535e-02, -3.0788e-02, -4.7044e-02, 1.5876e-02,\n",
|
|||
|
" -1.1296e-03, -1.8713e-02, 1.8543e-02, 5.3209e-02, 2.7803e-02,\n",
|
|||
|
" 1.1028e-03, 2.9207e-02, -3.6119e-02, -1.6165e-02, 1.7555e-02,\n",
|
|||
|
" -8.2125e-03, 6.3445e-03, -4.5027e-02, -4.0817e-02, 4.5773e-02,\n",
|
|||
|
" -2.2641e-02, 5.2889e-02, 1.4512e-02, -1.9522e-02, -5.6481e-02,\n",
|
|||
|
" -1.1060e-02, -4.3722e-02, 1.4095e-02, 2.4259e-02, 6.0377e-02,\n",
|
|||
|
" -7.2628e-02, 3.9760e-02, -8.1585e-02, -9.1420e-03, 1.8809e-03,\n",
|
|||
|
" 1.0487e-01, -4.9327e-02, -5.5549e-03, 4.6258e-02, 1.6701e-02,\n",
|
|||
|
" -1.6163e-02, 2.6286e-02, -2.7700e-02, 8.0984e-03, 2.4454e-02,\n",
|
|||
|
" 4.4797e-02, 3.4455e-02, -6.0768e-02, -2.5864e-02, 1.0166e-03,\n",
|
|||
|
" 5.3068e-03, 4.2425e-03, -5.4753e-02, -3.1478e-02, 3.7924e-02,\n",
|
|||
|
" 1.0266e-03, 8.4248e-03, 4.5199e-02, 1.5580e-02, -5.6708e-03,\n",
|
|||
|
" 3.6769e-02, -5.3641e-02, 4.1779e-02, -3.2060e-02, -2.6757e-02,\n",
|
|||
|
" -4.7505e-02, -8.2457e-02, 7.2944e-02, 1.5763e-02, 1.6309e-02,\n",
|
|||
|
" 7.5724e-04, 6.4329e-02, 1.9464e-02, -2.7392e-03, 4.2363e-02,\n",
|
|||
|
" -1.0416e-01, -1.6209e-02, 4.5399e-02, 1.0589e-01, -2.4638e-02,\n",
|
|||
|
" 2.6731e-02, 2.6622e-02, 4.0844e-02, 1.0323e-01, 3.3835e-02,\n",
|
|||
|
" -4.2006e-02, 4.2951e-02, 3.1068e-02, -2.4564e-03, -1.2811e-02,\n",
|
|||
|
" -8.4661e-03, 3.9647e-02, -1.1733e-01, 2.6631e-02, -3.1336e-02,\n",
|
|||
|
" -1.0026e-01, -6.3246e-03, -1.8747e-02, -8.8051e-03, -6.3902e-02,\n",
|
|||
|
" 2.0967e-01, 3.4409e-02, -1.6454e-02, 3.0606e-02, -2.1813e-02,\n",
|
|||
|
" 9.1961e-02, 4.4120e-02, -2.1517e-02, -3.4456e-02, -5.8409e-02,\n",
|
|||
|
" 2.7488e-02, 1.9422e-02, -1.2918e-02, -7.2962e-03, 2.8859e-02,\n",
|
|||
|
" -4.2516e-02, -4.2966e-02, -1.9645e-02, -6.4296e-02, -4.0894e-02,\n",
|
|||
|
" -2.8706e-02, -5.6346e-02, 3.4201e-02, -3.9250e-03, 7.5307e-02,\n",
|
|||
|
" 6.5123e-03, -4.7450e-02, -3.1443e-02, -5.0485e-02, 6.2536e-02,\n",
|
|||
|
" -2.6723e-02, 3.9097e-02, 2.5871e-03, 4.8988e-02, -1.2248e-05,\n",
|
|||
|
" 1.8120e-02, -1.4111e-02, -2.9327e-02, 7.4617e-02, 8.2369e-03,\n",
|
|||
|
" -3.3414e-02, -1.0466e-02, -4.4706e-03, -1.3613e-02, -5.4163e-02,\n",
|
|||
|
" -4.4011e-02, -7.4851e-02, -5.5124e-02, 8.7570e-03, 3.8449e-02,\n",
|
|||
|
" -5.1844e-02, 5.9674e-03, 7.5129e-03, 1.0718e-02, 2.1981e-02,\n",
|
|||
|
" 4.4945e-02, -3.4382e-02, -5.1930e-02, 1.5666e-02, -3.3479e-02,\n",
|
|||
|
" -2.9640e-03, -1.2958e-02, -3.5843e-02, -2.9896e-02, 7.1761e-02,\n",
|
|||
|
" -3.2109e-02, 1.1761e-01, 1.1047e-02, -4.7208e-02, -3.3970e-02,\n",
|
|||
|
" 7.1073e-02, -9.1408e-02, 6.3568e-03, -7.5566e-03, -8.2016e-03,\n",
|
|||
|
" -9.3746e-03, 1.5221e-02, 7.5551e-03, -4.2618e-02, 2.9687e-02,\n",
|
|||
|
" 4.7213e-02, -5.6087e-02, -3.5213e-02, -6.5220e-02, 1.8469e-02,\n",
|
|||
|
" 6.4949e-02, -1.9809e-02, -8.2783e-02, -3.2709e-03, -5.1782e-02,\n",
|
|||
|
" -6.3309e-02, 3.6822e-02, -8.6364e-04, -1.7256e-02, 6.0698e-03,\n",
|
|||
|
" -2.0665e-02, 1.7764e-02, 8.8567e-02, -5.4184e-02, -1.4816e-02,\n",
|
|||
|
" 6.1665e-02, 2.7374e-02, 2.2000e+01, 9.0000e+00, 2.2000e+01,\n",
|
|||
|
" 1.7000e+01, 1.2000e+01, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 3.0000e+00, 1.6000e+01, 2.1000e+01,\n",
|
|||
|
" 2.0000e+00, 3.2000e+01, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 3.0000e+00, 1.0000e+00, 2.0000e+00,\n",
|
|||
|
" 3.0000e+00, 4.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00])\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"x = []\n",
|
|||
|
"for indeks_wersu_pierwszego in range(len(zanurzenia_treningowe_in)):\n",
|
|||
|
" x.append(torch.cat(\n",
|
|||
|
" (\n",
|
|||
|
" zanurzenia_treningowe_in[indeks_wersu_pierwszego],\n",
|
|||
|
" torch.from_numpy(poczatki_treningowe_in[indeks_wersu_pierwszego]),\n",
|
|||
|
" torch.from_numpy(konce_treningowe_in[indeks_wersu_pierwszego]),\n",
|
|||
|
" torch.from_numpy(tony_treningowe_in[indeks_wersu_pierwszego])\n",
|
|||
|
" )\n",
|
|||
|
" ))\n",
|
|||
|
"print(len(x))\n",
|
|||
|
"print(x[0].shape)\n",
|
|||
|
"print(x[0])"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "EhjbWOLFzjRY"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Tensory - reprezentacje drugich wersów wierszy treningowych."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 74,
|
|||
|
"metadata": {
|
|||
|
"id": "jIknbIc-zjRY",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "745ce0bb-8284-465e-b4c4-2dcaaf090ce6"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"5959\n",
|
|||
|
"torch.Size([617])\n",
|
|||
|
"tensor([ 2.4096e-02, 7.3348e-02, -8.4988e-03, 2.1168e-02, -5.1689e-02,\n",
|
|||
|
" -3.5376e-04, 4.7075e-02, 2.5451e-02, 3.5129e-02, 4.6819e-02,\n",
|
|||
|
" 6.8763e-02, -2.2109e-01, -2.9449e-02, 5.0597e-02, -2.5865e-02,\n",
|
|||
|
" -9.3008e-03, 2.8629e-02, -6.2801e-02, -6.8237e-02, -5.9068e-02,\n",
|
|||
|
" 2.1109e-02, -3.3667e-02, -3.0538e-02, 1.0535e-01, 3.0778e-02,\n",
|
|||
|
" -2.6921e-02, -4.7817e-03, 2.0352e-02, -6.7792e-02, 6.7208e-02,\n",
|
|||
|
" 1.7218e-02, 1.9034e-02, -5.1180e-02, -1.4875e-02, -1.5020e-02,\n",
|
|||
|
" -1.2319e-02, -6.5349e-02, 4.0683e-02, 5.6421e-02, -2.2507e-02,\n",
|
|||
|
" -2.5330e-02, 4.9632e-02, 5.9727e-02, 9.1537e-03, -2.7953e-02,\n",
|
|||
|
" -4.3726e-02, -3.3593e-02, 1.8592e-02, 1.5352e-03, -9.2273e-03,\n",
|
|||
|
" -6.5650e-02, 3.0612e-02, -6.9992e-02, -2.7435e-02, 2.9220e-02,\n",
|
|||
|
" -3.2722e-02, -3.1333e-02, 1.2232e-02, -6.3038e-02, 6.2572e-04,\n",
|
|||
|
" 2.0118e-02, -4.7327e-02, -4.6759e-02, 1.6298e-02, 2.4694e-02,\n",
|
|||
|
" -1.5708e-02, -1.7262e-02, -1.1750e-02, -3.4596e-03, -5.3582e-02,\n",
|
|||
|
" -8.0472e-02, 5.7651e-02, 3.8062e-02, -7.1649e-02, 4.5374e-02,\n",
|
|||
|
" -7.1557e-02, 1.8123e-02, 3.5019e-02, -8.7280e-02, -5.9952e-03,\n",
|
|||
|
" 1.3746e-02, 1.6378e-02, -4.3599e-02, 1.0333e-02, -1.3245e-02,\n",
|
|||
|
" -3.2981e-02, -6.4206e-02, -2.4593e-02, 3.1208e-02, -9.5114e-03,\n",
|
|||
|
" -5.2171e-02, -4.6604e-02, 5.0359e-02, 4.7381e-02, -7.6541e-03,\n",
|
|||
|
" 1.7540e-02, -7.5362e-03, -1.0370e-03, -2.0973e-02, -5.8539e-02,\n",
|
|||
|
" -4.2109e-03, -7.7784e-02, 4.7974e-02, 1.5605e-02, 1.1676e-02,\n",
|
|||
|
" 5.4789e-02, 2.6982e-02, 2.8896e-02, 1.4084e-02, 3.6774e-02,\n",
|
|||
|
" -5.7120e-02, 8.5216e-02, -1.8359e-02, 1.8367e-02, -5.9878e-02,\n",
|
|||
|
" 2.1155e-02, 3.2800e-03, 1.5960e-02, -1.3590e-01, 6.6871e-02,\n",
|
|||
|
" 5.0083e-03, 4.7189e-03, 9.8846e-02, -4.0727e-02, -1.1970e-01,\n",
|
|||
|
" 4.3001e-03, -3.3519e-02, -1.2028e-02, 5.3046e-02, 6.3472e-02,\n",
|
|||
|
" 8.0517e-03, -1.6034e-02, 1.1180e-02, -2.7315e-02, -1.9381e-02,\n",
|
|||
|
" -2.0683e-02, 3.7952e-03, -7.2708e-02, -3.0257e-02, 7.5861e-03,\n",
|
|||
|
" -3.0704e-02, -7.9766e-03, 9.0976e-03, -6.8949e-02, 9.3395e-02,\n",
|
|||
|
" -5.1396e-02, 4.6734e-02, -1.2085e-03, 2.2747e-02, 4.4702e-02,\n",
|
|||
|
" -1.9269e-02, -3.2044e-02, 4.6390e-02, 5.6546e-02, -3.7156e-02,\n",
|
|||
|
" 3.9877e-02, 1.0895e-02, -1.6061e-02, -6.7260e-02, 1.6562e-02,\n",
|
|||
|
" 1.2008e-03, 3.7859e-02, 3.9005e-02, 3.4202e-02, -1.4327e-02,\n",
|
|||
|
" -8.2659e-02, 1.9792e-02, 1.5776e-03, -6.7330e-02, 4.3296e-02,\n",
|
|||
|
" -4.3103e-02, -8.2537e-03, 3.0699e-02, -1.7245e-02, 5.5340e-02,\n",
|
|||
|
" -7.3155e-03, 2.0148e-02, -2.6217e-02, -1.6741e-03, 7.1648e-02,\n",
|
|||
|
" 2.5549e-02, 2.2865e-02, -2.0414e-03, -1.6362e-02, 4.6387e-03,\n",
|
|||
|
" 2.8256e-02, 2.3293e-02, -2.2062e-02, -9.2340e-03, -1.1985e-02,\n",
|
|||
|
" 8.0533e-04, -2.3884e-02, 5.9400e-02, -1.1038e-02, 4.8180e-03,\n",
|
|||
|
" 3.5944e-02, -6.4729e-02, -1.1301e-02, -5.6865e-02, 1.8658e-02,\n",
|
|||
|
" -1.4537e-02, -2.3870e-02, 1.8639e-02, 6.1247e-02, 1.8494e-03,\n",
|
|||
|
" 3.9511e-03, -1.1623e-02, 2.7783e-02, -9.0809e-02, -4.3361e-02,\n",
|
|||
|
" -4.4524e-02, 9.5100e-03, 8.1598e-03, -5.9092e-02, 2.2854e-02,\n",
|
|||
|
" 1.0801e-02, 5.5640e-02, -7.4158e-03, -3.0120e-02, -4.7106e-02,\n",
|
|||
|
" -2.8703e-02, 6.2336e-02, -8.6966e-02, -8.8282e-02, -2.9747e-02,\n",
|
|||
|
" -2.8669e-02, 2.8053e-02, -3.0225e-02, -2.4561e-02, -1.2942e-02,\n",
|
|||
|
" -4.3129e-02, -5.1436e-02, 3.2625e-02, -4.6949e-02, -1.2704e-02,\n",
|
|||
|
" 2.7554e-02, 1.4629e-02, 3.8203e-02, -8.7354e-02, -2.7942e-02,\n",
|
|||
|
" -4.2217e-02, 4.5440e-02, -1.1199e-02, 1.5859e-02, -5.7629e-02,\n",
|
|||
|
" -3.4809e-02, -5.4919e-02, 1.9037e-02, 1.0293e-02, 6.9702e-03,\n",
|
|||
|
" -3.0121e-02, 7.6800e-02, -1.9755e-02, -1.2176e-01, -4.2284e-02,\n",
|
|||
|
" -5.6440e-02, -3.4314e-02, -3.0538e-02, -5.3078e-02, -2.0438e-02,\n",
|
|||
|
" -2.7687e-03, 1.5685e-02, 8.3713e-03, 1.4941e-02, 2.8835e-02,\n",
|
|||
|
" -1.5773e-02, -2.2957e-02, 3.4821e-02, 8.3100e-03, -3.6987e-02,\n",
|
|||
|
" 1.0159e-03, 3.6687e-02, 1.5403e-02, -7.7245e-02, 1.1903e-02,\n",
|
|||
|
" 3.9656e-02, 5.8933e-02, 1.1769e-03, -7.7724e-03, 1.0608e-01,\n",
|
|||
|
" -1.3163e-02, -6.9340e-03, -2.9777e-02, 3.8290e-02, 2.5452e-02,\n",
|
|||
|
" -4.4490e-02, -1.2190e-01, -9.1041e-03, 8.4519e-03, -1.0265e-03,\n",
|
|||
|
" 3.0511e-02, -4.8933e-02, 3.1984e-03, 1.9107e-02, -1.9031e-02,\n",
|
|||
|
" -2.7986e-02, 2.8155e-02, -3.2111e-02, 5.3439e-02, -6.6016e-02,\n",
|
|||
|
" 2.2510e-02, -2.5893e-02, 2.5647e-02, 6.2114e-02, 3.6392e-03,\n",
|
|||
|
" 2.1521e-02, 1.0638e-03, 4.0820e-02, -2.1784e-02, 2.3471e-02,\n",
|
|||
|
" 6.5689e-03, 4.1211e-02, 2.2548e-02, -6.9367e-02, 7.2007e-02,\n",
|
|||
|
" -2.3249e-02, 9.7457e-03, 5.0128e-03, 1.9682e-03, 1.1460e-02,\n",
|
|||
|
" -1.1636e-03, 1.2196e-02, -8.2566e-03, -1.2993e-02, 4.0637e-02,\n",
|
|||
|
" -1.2862e-02, -9.3435e-03, 3.5840e-02, -1.3115e-02, 6.7564e-02,\n",
|
|||
|
" -1.3449e-02, 8.3304e-02, 1.3780e-02, -6.5205e-03, 2.1614e-02,\n",
|
|||
|
" -4.6509e-02, -2.3400e-02, -1.1252e-02, -2.1349e-03, 9.9767e-02,\n",
|
|||
|
" 5.9413e-02, -6.5736e-03, -4.4302e-02, 1.0448e-02, -1.8580e-02,\n",
|
|||
|
" 6.8594e-02, -1.4184e-02, -7.0092e-02, -3.2865e-02, 1.1723e-02,\n",
|
|||
|
" 9.1901e-03, -1.5251e-02, -1.4926e-02, -3.3470e-02, -3.6489e-03,\n",
|
|||
|
" -3.8432e-02, 1.9594e-02, 2.5313e-02, -4.9300e-02, 6.5015e-02,\n",
|
|||
|
" -3.0438e-02, -9.3662e-03, 3.4233e-02, -7.8762e-02, -6.7159e-03,\n",
|
|||
|
" 3.1354e-02, -2.0526e-02, 5.4133e-03, 1.1246e-02, 2.1658e-02,\n",
|
|||
|
" -1.0054e-02, 2.1285e-02, 1.1186e-01, -3.7673e-02, 2.4505e-02,\n",
|
|||
|
" -2.0750e-02, -3.7844e-02, -2.8911e-02, 9.4496e-03, 1.4896e-02,\n",
|
|||
|
" -3.0971e-02, -1.8133e-02, -4.7326e-02, -2.8264e-02, 4.9661e-02,\n",
|
|||
|
" -1.8136e-02, -2.1942e-02, -2.6936e-02, 2.0541e-02, 4.2219e-03,\n",
|
|||
|
" 6.6803e-02, -6.7906e-02, -3.7795e-02, -2.2262e-02, 3.3751e-02,\n",
|
|||
|
" 1.1071e-02, 4.1053e-02, -6.2190e-02, 4.3035e-03, -3.6697e-02,\n",
|
|||
|
" -2.4697e-03, 3.2390e-02, -6.7590e-02, 3.7872e-02, 3.5083e-02,\n",
|
|||
|
" -4.1133e-02, 1.5301e-02, -9.9107e-03, -5.2390e-02, 6.0837e-02,\n",
|
|||
|
" 2.2806e-01, -7.3393e-02, 2.9662e-02, -6.6508e-02, -1.7553e-02,\n",
|
|||
|
" 7.5196e-02, -3.6798e-02, 4.5125e-03, 3.4317e-02, -5.6979e-02,\n",
|
|||
|
" 5.9627e-02, 7.8637e-02, -6.2195e-02, 4.5452e-02, -2.8716e-03,\n",
|
|||
|
" 8.0530e-02, -1.8484e-02, 2.2444e-02, -2.6805e-02, -2.2107e-02,\n",
|
|||
|
" -2.1742e-02, -3.0206e-02, 7.3662e-02, 4.2316e-02, 4.5892e-02,\n",
|
|||
|
" -2.8320e-02, 6.5208e-02, -4.3190e-02, -5.5195e-02, -7.3266e-02,\n",
|
|||
|
" -1.6800e-03, 4.9327e-02, 3.7236e-02, 1.3723e-02, 2.8840e-02,\n",
|
|||
|
" 9.9783e-03, -4.3477e-02, 2.6408e-02, -5.9908e-03, 3.1495e-02,\n",
|
|||
|
" -1.3816e-03, 1.8268e-02, -2.0290e-02, -7.3615e-02, -4.2263e-02,\n",
|
|||
|
" 3.5367e-02, -4.4292e-02, -7.9611e-02, 7.9907e-02, 4.5494e-02,\n",
|
|||
|
" -3.2248e-02, 1.6629e-02, -7.5351e-03, 2.1802e-02, -3.3684e-02,\n",
|
|||
|
" -1.4436e-02, 2.1520e-02, -6.3879e-02, 1.0100e-02, -2.5601e-05,\n",
|
|||
|
" -1.9271e-02, 4.7454e-02, -2.4488e-02, 6.5203e-03, 5.9140e-02,\n",
|
|||
|
" 3.7843e-02, 3.8729e-02, 3.5719e-02, 6.4549e-02, 3.9975e-02,\n",
|
|||
|
" -7.7090e-03, -3.8202e-02, -4.2739e-02, 6.9333e-02, -3.2327e-02,\n",
|
|||
|
" 1.3822e-01, 7.5231e-03, 1.8590e-02, -2.8336e-02, 7.5397e-02,\n",
|
|||
|
" -8.1537e-03, -7.2928e-02, -6.6228e-02, 1.4838e-02, -2.3286e-02,\n",
|
|||
|
" 4.9019e-02, 1.8467e-02, -6.7986e-02, -4.8970e-02, -2.9831e-02,\n",
|
|||
|
" 4.9185e-02, 3.9403e-03, 6.8458e-02, 4.9250e-02, -9.2371e-02,\n",
|
|||
|
" -1.7414e-02, 3.7454e-02, 4.5524e-02, -4.9280e-02, 5.0603e-02,\n",
|
|||
|
" 5.4588e-03, -5.6567e-02, 1.3000e+01, 1.7000e+01, 2.1000e+01,\n",
|
|||
|
" 4.0000e+00, 8.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 1.9000e+01, 3.0000e+01, 4.0000e+00,\n",
|
|||
|
" 3.1000e+01, 1.2000e+01, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 1.0000e+00, 3.0000e+00, 4.0000e+00,\n",
|
|||
|
" 1.0000e+00, 2.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00])\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"y = []\n",
|
|||
|
"for indeks_wersu_pierwszego in range(len(zanurzenia_treningowe_out)):\n",
|
|||
|
" y.append(\n",
|
|||
|
" torch.cat(\n",
|
|||
|
" (\n",
|
|||
|
" zanurzenia_treningowe_out[indeks_wersu_pierwszego],\n",
|
|||
|
" torch.from_numpy(poczatki_treningowe_out[indeks_wersu_pierwszego]),\n",
|
|||
|
" torch.from_numpy(konce_treningowe_out[indeks_wersu_pierwszego]),\n",
|
|||
|
" torch.from_numpy(tony_treningowe_out[indeks_wersu_pierwszego])\n",
|
|||
|
" )\n",
|
|||
|
" )\n",
|
|||
|
" )\n",
|
|||
|
"print(len(y))\n",
|
|||
|
"print(y[0].shape)\n",
|
|||
|
"print(y[0])"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "hSsSQpJkzjRY"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"## Wejście do sieci neuronowej.\n",
|
|||
|
"### Odpowiadające sobie wersy i kilka losowo dobranych nieodpowiadających sobie wersów."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 75,
|
|||
|
"metadata": {
|
|||
|
"id": "dmr8EtB6zjRY",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "8e51bc79-8e60-4ec0-87c2-404248f715f9"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"17877\n",
|
|||
|
"tensor([-0.0314, 0.0429, -0.0142, ..., 0.0000, 0.0000, 0.0000])\n",
|
|||
|
"17877\n",
|
|||
|
"1\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"from random import sample\n",
|
|||
|
"X = []\n",
|
|||
|
"Y = []\n",
|
|||
|
"for indeks_wersu_drugiego in range(len(x)):\n",
|
|||
|
" indeksy = sample(range(len(y)), 3)\n",
|
|||
|
" if indeks_wersu_drugiego not in indeksy:\n",
|
|||
|
" indeksy[0] = indeks_wersu_drugiego\n",
|
|||
|
" for k in indeksy:\n",
|
|||
|
" X.append(\n",
|
|||
|
" torch.cat(\n",
|
|||
|
" (x[indeks_wersu_drugiego], y[k])\n",
|
|||
|
" )\n",
|
|||
|
" )\n",
|
|||
|
" if indeks_wersu_drugiego==k:\n",
|
|||
|
" Y.append(1)\n",
|
|||
|
" else:\n",
|
|||
|
" Y.append(0)\n",
|
|||
|
"\n",
|
|||
|
"print(len(X))\n",
|
|||
|
"print(X[0])\n",
|
|||
|
"print(len(Y))\n",
|
|||
|
"print(Y[0])"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "CAIPJDIRzjRZ"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"## Modele sklearn."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 76,
|
|||
|
"metadata": {
|
|||
|
"id": "rOSL9ad8zjRZ",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/",
|
|||
|
"height": 74
|
|||
|
},
|
|||
|
"outputId": "26a03bcb-5e80-4388-e4e5-45c5902f5fa0"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "execute_result",
|
|||
|
"data": {
|
|||
|
"text/plain": [
|
|||
|
"MLPClassifier()"
|
|||
|
],
|
|||
|
"text/html": [
|
|||
|
"<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-con
|
|||
|
]
|
|||
|
},
|
|||
|
"metadata": {},
|
|||
|
"execution_count": 76
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"from sklearn.neural_network import MLPClassifier\n",
|
|||
|
"klasyfikator = MLPClassifier() # activation=\"tanh\"\n",
|
|||
|
"\n",
|
|||
|
"klasyfikator.fit(X, Y)"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 77,
|
|||
|
"metadata": {
|
|||
|
"id": "ArwFdmitzjRZ",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/",
|
|||
|
"height": 74
|
|||
|
},
|
|||
|
"outputId": "0aa58eb1-494a-4dff-f3e9-40c000e281ba"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "execute_result",
|
|||
|
"data": {
|
|||
|
"text/plain": [
|
|||
|
"MLPRegressor()"
|
|||
|
],
|
|||
|
"text/html": [
|
|||
|
"<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-con
|
|||
|
]
|
|||
|
},
|
|||
|
"metadata": {},
|
|||
|
"execution_count": 77
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"from sklearn.neural_network import MLPRegressor\n",
|
|||
|
"regresor = MLPRegressor() # activation=\"tanh\"\n",
|
|||
|
"\n",
|
|||
|
"regresor.fit(X, Y)"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "zSkdihWOzjRZ"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Początki, końcówki i tony wierszy testowych."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 78,
|
|||
|
"metadata": {
|
|||
|
"id": "fXm6rEuVzjRZ",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "01ef5db7-ddf3-40f6-b43a-ba4104b9a732"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"1490\n",
|
|||
|
"林霭渐浓迷古寺\n",
|
|||
|
"尘烟已远隐青山\n",
|
|||
|
"1490\n",
|
|||
|
"[ 8 22 12 7 3 9 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
|
|||
|
" 0 0 0 0 0 0 0 0 0 0 0]\n",
|
|||
|
"[25 12 21 34 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
|
|||
|
" 0 0 0 0 0 0 0 0 0 0 0]\n",
|
|||
|
"[2 3 4 2 2 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
|
|||
|
"[16 22 22 22 22 13 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
|
|||
|
" 0 0 0 0 0 0 0 0 0 0 0]\n",
|
|||
|
"[24 21 1 23 25 32 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
|
|||
|
" 0 0 0 0 0 0 0 0 0 0 0]\n",
|
|||
|
"[2 1 3 3 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"print(len(testowe[\"in\"]))\n",
|
|||
|
"print(testowe[\"in\"][0])\n",
|
|||
|
"print(testowe[\"out\"][0])\n",
|
|||
|
"\n",
|
|||
|
"liczba_wejscia = 35\n",
|
|||
|
"\n",
|
|||
|
"poczatki_testowe_in, konce_testowe_in, tony_testowe_in = poczatki_konce_tony_dla_zdan(testowe[\"in\"], liczba_wejscia)\n",
|
|||
|
"poczatki_testowe_out, konce_testowe_out, tony_testowe_out = poczatki_konce_tony_dla_zdan(testowe[\"out\"], liczba_wejscia)\n",
|
|||
|
"\n",
|
|||
|
"print(len(poczatki_testowe_in))\n",
|
|||
|
"print(poczatki_testowe_in[0])\n",
|
|||
|
"print(konce_testowe_in[0])\n",
|
|||
|
"print(tony_testowe_in[0])\n",
|
|||
|
"print(poczatki_testowe_out[0])\n",
|
|||
|
"print(konce_testowe_out[0])\n",
|
|||
|
"print(tony_testowe_out[0])"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "eEGtSbSrzjRa"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Zanurzenia BAAI wierszy testowych."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 79,
|
|||
|
"metadata": {
|
|||
|
"id": "X5_MB-qxzjRa",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "cdc1adb7-a54b-4213-f8b0-d8fd630fa9b7"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"1490\n",
|
|||
|
"林霭渐浓迷古寺\n",
|
|||
|
"尘烟已远隐青山\n",
|
|||
|
"torch.Size([1490, 512])\n",
|
|||
|
"tensor([-0.0803, -0.0044, -0.0786, -0.0128, 0.0160, -0.0353, 0.0014, 0.0223,\n",
|
|||
|
" 0.0380, -0.0011, 0.0339, -0.2229, 0.0089, 0.0073, -0.0201, 0.0610,\n",
|
|||
|
" -0.0445, -0.0449, -0.0055, -0.0014, -0.0261, -0.0536, -0.0592, -0.0063,\n",
|
|||
|
" 0.0381, -0.0866, 0.0715, 0.0058, -0.0275, 0.0164, 0.0154, 0.0230,\n",
|
|||
|
" -0.0277, 0.0550, 0.0030, 0.0233, -0.0007, 0.0052, -0.1081, -0.0225,\n",
|
|||
|
" 0.0060, 0.0156, 0.0174, -0.0953, -0.0445, -0.0736, 0.0245, 0.0071,\n",
|
|||
|
" -0.0047, -0.0154, 0.0251, 0.0371, 0.0372, 0.0557, 0.0354, 0.0049,\n",
|
|||
|
" -0.0377, 0.0925, -0.0479, 0.0592, -0.0294, -0.0117, -0.0099, -0.0365,\n",
|
|||
|
" -0.0016, 0.0338, 0.0182, 0.0122, -0.0254, 0.0362, 0.0191, -0.0080,\n",
|
|||
|
" -0.0086, -0.0128, -0.0514, -0.0405, -0.0103, 0.0150, -0.0543, -0.0259,\n",
|
|||
|
" 0.0189, -0.0283, 0.0074, 0.0298, 0.0197, -0.0688, 0.0169, 0.0327,\n",
|
|||
|
" 0.0889, -0.0453, 0.0061, -0.0504, 0.0300, 0.0526, 0.0672, 0.0366,\n",
|
|||
|
" -0.0350, -0.0167, -0.0342, -0.0425, 0.0959, -0.0246, 0.0599, 0.0506,\n",
|
|||
|
" 0.0071, -0.0019, -0.0378, -0.0397, -0.0615, -0.0451, 0.0148, 0.0754,\n",
|
|||
|
" 0.0448, 0.0079, 0.0079, -0.0323, 0.0420, 0.0261, -0.0189, -0.0006,\n",
|
|||
|
" -0.0555, -0.0707, 0.0748, -0.0446, 0.0205, 0.0555, -0.0474, -0.0284,\n",
|
|||
|
" -0.0078, 0.0699, 0.0321, -0.0116, 0.0031, 0.0383, -0.0438, -0.0464,\n",
|
|||
|
" -0.0280, 0.0207, -0.0657, 0.0622, -0.0342, -0.0725, -0.0604, 0.0033,\n",
|
|||
|
" 0.0161, -0.0520, 0.0893, 0.0283, 0.0264, 0.0397, 0.0252, 0.0352,\n",
|
|||
|
" -0.0568, -0.0103, 0.0428, -0.0079, 0.0340, 0.0678, -0.0089, -0.0216,\n",
|
|||
|
" 0.0258, -0.0137, -0.0024, -0.0292, -0.0136, -0.0459, -0.0651, -0.0068,\n",
|
|||
|
" -0.0241, 0.0677, -0.0161, -0.0421, -0.0768, 0.0280, 0.0130, -0.0030,\n",
|
|||
|
" -0.0158, 0.0035, -0.0023, 0.0578, -0.0297, 0.0666, -0.0248, 0.0051,\n",
|
|||
|
" -0.0228, 0.0210, -0.0042, -0.0209, -0.0648, -0.0295, 0.0431, 0.0029,\n",
|
|||
|
" 0.0094, -0.0634, 0.0191, 0.0710, -0.0919, -0.0410, -0.1001, 0.0639,\n",
|
|||
|
" -0.0363, -0.0589, 0.0432, 0.0345, -0.0109, 0.0306, -0.0363, 0.0605,\n",
|
|||
|
" -0.0013, -0.0236, -0.0073, 0.0215, -0.0072, -0.0054, 0.0342, 0.1021,\n",
|
|||
|
" -0.0012, -0.0165, -0.0412, -0.0310, 0.0153, 0.0478, 0.0047, -0.0200,\n",
|
|||
|
" 0.0466, -0.0296, 0.0525, -0.0086, -0.0481, 0.0070, 0.0184, 0.0016,\n",
|
|||
|
" -0.0953, 0.0013, 0.1069, 0.0215, -0.0232, 0.0104, -0.0105, -0.0317,\n",
|
|||
|
" -0.0467, 0.0119, 0.0255, -0.0118, -0.0739, -0.0692, -0.0154, -0.0009,\n",
|
|||
|
" 0.0805, 0.0470, -0.0154, -0.0147, 0.0111, -0.0048, 0.0023, -0.0210,\n",
|
|||
|
" 0.0198, -0.0203, 0.0076, 0.0339, 0.0109, 0.0072, 0.0375, 0.0244,\n",
|
|||
|
" 0.0248, 0.0157, -0.0538, 0.1174, -0.0760, -0.0135, 0.0005, 0.0435,\n",
|
|||
|
" -0.0583, 0.0124, -0.0299, 0.0655, 0.0473, 0.0527, -0.0647, 0.0033,\n",
|
|||
|
" -0.0037, 0.0615, -0.0907, 0.0393, -0.0100, -0.0449, -0.1082, -0.0489,\n",
|
|||
|
" 0.0798, -0.0139, 0.0306, -0.0693, 0.0855, 0.0304, -0.0006, -0.0617,\n",
|
|||
|
" 0.0730, -0.0322, 0.0346, 0.0150, 0.0505, -0.0537, -0.0049, -0.0557,\n",
|
|||
|
" -0.0587, -0.0152, 0.0275, 0.0546, -0.0402, 0.0414, 0.0082, 0.0187,\n",
|
|||
|
" 0.0807, 0.0023, -0.0020, -0.0127, 0.0018, 0.0367, -0.0196, 0.0370,\n",
|
|||
|
" 0.0481, 0.0114, -0.0740, -0.0470, 0.0473, -0.0203, 0.0007, -0.0120,\n",
|
|||
|
" 0.0184, 0.0408, 0.0107, -0.0040, 0.0381, -0.0439, -0.0488, -0.1110,\n",
|
|||
|
" -0.0242, -0.0229, 0.0843, 0.0632, 0.0496, 0.0440, -0.0541, 0.0328,\n",
|
|||
|
" 0.0049, 0.0339, -0.0236, 0.0681, -0.0083, 0.0119, -0.0179, -0.0340,\n",
|
|||
|
" 0.0168, 0.0519, 0.0075, -0.0363, -0.0171, 0.0245, 0.0448, 0.0626,\n",
|
|||
|
" 0.0217, 0.0014, -0.0181, -0.0617, 0.0774, -0.0584, -0.0292, 0.0295,\n",
|
|||
|
" -0.0710, -0.0215, -0.0300, -0.0251, -0.0351, -0.0061, 0.0562, -0.0010,\n",
|
|||
|
" 0.0253, 0.0533, 0.0115, 0.0012, -0.0555, 0.0206, 0.0161, -0.0110,\n",
|
|||
|
" 0.0324, -0.0452, -0.0211, 0.0295, 0.0695, -0.0363, 0.0241, -0.0955,\n",
|
|||
|
" 0.0015, 0.0520, 0.0293, -0.0128, 0.0318, -0.0065, 0.0288, -0.0172,\n",
|
|||
|
" 0.0413, -0.0386, -0.0374, -0.0453, -0.0624, -0.0277, 0.0209, 0.0129,\n",
|
|||
|
" 0.0102, -0.0380, 0.2030, -0.0521, -0.0468, 0.0020, 0.0141, 0.0326,\n",
|
|||
|
" -0.0218, -0.0495, -0.0097, -0.0504, 0.0061, 0.1062, -0.0181, -0.0192,\n",
|
|||
|
" -0.0529, 0.0135, -0.0018, 0.0083, -0.0582, -0.0124, 0.0261, 0.0147,\n",
|
|||
|
" 0.0661, 0.0707, 0.0129, 0.0510, 0.0094, 0.0139, -0.0332, 0.0405,\n",
|
|||
|
" -0.0319, 0.0064, 0.0060, -0.0278, -0.0744, -0.0532, -0.0796, -0.0301,\n",
|
|||
|
" 0.0271, -0.0158, -0.0048, -0.0131, -0.0572, 0.0206, 0.0347, 0.0211,\n",
|
|||
|
" -0.0953, -0.0821, 0.0239, 0.0533, -0.0734, -0.0091, -0.0394, 0.0181,\n",
|
|||
|
" -0.0606, 0.0127, -0.0173, -0.0278, -0.0144, 0.0172, 0.0281, -0.0363,\n",
|
|||
|
" -0.0219, 0.0014, 0.0365, 0.0259, 0.0199, -0.0597, -0.0501, -0.0056,\n",
|
|||
|
" -0.0631, -0.0121, -0.0168, -0.0255, 0.0857, 0.0378, 0.0286, 0.0531,\n",
|
|||
|
" -0.0618, -0.0443, -0.0697, -0.0020, 0.0079, -0.0031, -0.0016, 0.0083,\n",
|
|||
|
" 0.0450, -0.0572, -0.0373, 0.0035, 0.0904, -0.0523, 0.0262, 0.0277,\n",
|
|||
|
" -0.0096, -0.0129, 0.1094, 0.0445, -0.0495, -0.0920, 0.0300, -0.0253])\n",
|
|||
|
"tensor([-4.2662e-02, -3.1708e-02, -5.6512e-02, 4.0084e-02, -3.8748e-02,\n",
|
|||
|
" 9.6097e-03, 4.7008e-02, 7.5668e-02, -1.3552e-02, 1.1450e-02,\n",
|
|||
|
" 7.6131e-02, -2.4726e-01, -2.2119e-02, -3.7263e-02, -1.6699e-03,\n",
|
|||
|
" -2.8011e-03, -7.1475e-03, 1.0944e-02, 1.0504e-02, -1.9638e-02,\n",
|
|||
|
" 3.1485e-02, -6.9084e-03, -2.0084e-02, -2.8139e-02, -2.1698e-02,\n",
|
|||
|
" -4.5725e-02, 6.2876e-02, -2.4744e-02, -5.4077e-02, 1.1777e-02,\n",
|
|||
|
" 3.2030e-02, -6.9665e-03, 1.5294e-02, -8.0923e-02, 2.8682e-02,\n",
|
|||
|
" 6.8040e-03, -3.3103e-03, 2.6560e-02, -6.0841e-02, 2.1151e-03,\n",
|
|||
|
" -3.2793e-02, 1.4114e-02, -3.0558e-02, -6.7261e-02, -7.8080e-02,\n",
|
|||
|
" -5.6005e-02, -2.4466e-02, 3.1131e-02, 1.2249e-02, -2.6226e-02,\n",
|
|||
|
" 5.6157e-03, -1.4219e-02, 6.1117e-02, 8.7854e-02, 3.2294e-02,\n",
|
|||
|
" -7.5786e-02, -3.8687e-03, 5.8290e-02, -1.8115e-02, 9.2157e-03,\n",
|
|||
|
" -1.9775e-02, -1.4765e-02, -8.1864e-02, -5.2147e-02, -3.3906e-02,\n",
|
|||
|
" 4.7297e-02, 1.7286e-02, -3.8963e-02, -7.2971e-03, 4.6875e-02,\n",
|
|||
|
" -7.3317e-02, 2.3185e-02, -1.0216e-02, -4.6695e-02, -1.0624e-02,\n",
|
|||
|
" -8.6403e-02, 1.0679e-02, 3.8384e-02, -2.0390e-02, -4.8561e-02,\n",
|
|||
|
" 1.7503e-02, -2.4822e-02, -1.0118e-01, -1.1536e-02, 1.2367e-02,\n",
|
|||
|
" -2.8817e-02, 3.8065e-02, -4.8299e-03, 3.6943e-02, -8.7723e-03,\n",
|
|||
|
" -3.3175e-02, -2.9473e-02, -1.4506e-02, 4.3669e-02, -1.3243e-02,\n",
|
|||
|
" 9.1637e-03, -5.2170e-02, 6.5091e-02, -6.0895e-03, -8.3091e-02,\n",
|
|||
|
" 4.1754e-03, -8.1591e-03, 1.3681e-02, 4.9336e-02, 5.1179e-02,\n",
|
|||
|
" 3.0331e-02, 8.2378e-03, 3.4116e-03, 8.7714e-03, 2.6082e-02,\n",
|
|||
|
" -5.9453e-02, 5.4098e-02, 1.5604e-02, 6.1685e-02, -6.5803e-02,\n",
|
|||
|
" -4.3957e-03, 2.8998e-02, 7.4083e-02, -7.5646e-02, 9.9494e-03,\n",
|
|||
|
" -3.0923e-02, -4.0035e-03, 2.6690e-02, -1.7936e-02, 5.9101e-02,\n",
|
|||
|
" -2.6199e-02, -8.9598e-03, -3.3770e-02, 2.5304e-02, -2.2467e-02,\n",
|
|||
|
" -8.5894e-03, -6.5153e-02, -1.8516e-02, -3.2236e-02, -6.9214e-02,\n",
|
|||
|
" -6.8273e-02, -6.8416e-03, 3.0168e-02, 2.6452e-02, 2.7235e-02,\n",
|
|||
|
" -1.4605e-02, 4.0644e-02, -3.1382e-02, -6.6872e-02, 3.6762e-02,\n",
|
|||
|
" 3.3090e-02, 4.1689e-02, 6.2443e-02, 1.1023e-01, 4.5009e-02,\n",
|
|||
|
" -5.9660e-02, -1.9799e-02, -2.1019e-02, -1.4585e-02, -4.9002e-02,\n",
|
|||
|
" 6.7098e-03, 4.8637e-02, 2.6957e-02, -1.2555e-01, -4.4153e-02,\n",
|
|||
|
" -3.7129e-02, -3.5815e-03, 3.7462e-03, -3.9305e-02, 6.6185e-03,\n",
|
|||
|
" -5.7863e-03, -4.5151e-02, 5.8374e-02, -7.9883e-02, 5.8874e-02,\n",
|
|||
|
" 2.9350e-02, -3.0508e-02, -5.7538e-02, 1.4630e-02, 1.7199e-02,\n",
|
|||
|
" -4.2483e-02, -6.1783e-02, 1.8635e-02, -5.8922e-03, 3.2036e-02,\n",
|
|||
|
" 1.1720e-02, 2.9453e-02, -4.1426e-02, -1.3919e-04, 7.4231e-02,\n",
|
|||
|
" 4.0965e-02, 5.2020e-02, -8.5824e-03, -5.0865e-02, 3.3741e-02,\n",
|
|||
|
" 3.1936e-02, -2.8163e-02, 2.5174e-02, -6.6295e-03, 5.4489e-03,\n",
|
|||
|
" 4.8801e-02, -3.4366e-02, 1.5345e-02, -4.9533e-02, 4.1175e-02,\n",
|
|||
|
" 1.0052e-02, -8.0770e-03, 1.6990e-03, 2.7498e-02, -2.0195e-02,\n",
|
|||
|
" 7.3058e-02, -1.3125e-02, 5.5075e-02, 1.0222e-02, -6.1044e-02,\n",
|
|||
|
" 1.6483e-02, 1.7518e-02, -3.5582e-02, 6.5297e-03, 4.8142e-02,\n",
|
|||
|
" -9.1526e-03, -2.5292e-02, -9.2555e-02, -2.7960e-02, -6.7617e-02,\n",
|
|||
|
" -2.7919e-02, 2.5797e-02, -3.3792e-02, -1.7559e-02, 5.0696e-03,\n",
|
|||
|
" 1.9502e-02, 9.8111e-02, -3.5467e-02, -7.8175e-03, -1.3069e-02,\n",
|
|||
|
" 1.1869e-02, -1.2426e-02, -3.8581e-02, 1.3946e-02, 8.5697e-02,\n",
|
|||
|
" 5.9456e-03, -7.0115e-03, 3.2226e-03, 3.0390e-03, -5.4671e-03,\n",
|
|||
|
" -4.7213e-02, 3.5392e-02, -1.5239e-03, 7.3812e-02, -1.7222e-02,\n",
|
|||
|
" -1.2772e-03, 2.1770e-02, -4.6258e-02, 3.5956e-02, 2.6292e-02,\n",
|
|||
|
" 5.5864e-03, 3.9837e-03, 1.4386e-02, -3.0420e-03, 1.6326e-02,\n",
|
|||
|
" -4.3116e-02, 6.0202e-04, -6.6577e-02, 4.4794e-02, -6.0236e-02,\n",
|
|||
|
" 3.3927e-03, 9.1377e-02, 6.7364e-02, -1.6208e-02, -4.1574e-02,\n",
|
|||
|
" 3.2682e-02, 7.2001e-03, 2.8854e-02, -2.5179e-02, -4.5137e-02,\n",
|
|||
|
" 5.7746e-03, 3.8036e-02, -1.8377e-02, 5.4035e-02, 1.5138e-02,\n",
|
|||
|
" 4.5383e-02, 1.1084e-02, -2.0204e-02, -1.0160e-02, 1.8784e-02,\n",
|
|||
|
" 9.1744e-03, 6.3865e-02, -6.9441e-02, -3.0443e-02, 1.0225e-02,\n",
|
|||
|
" 9.9409e-03, -1.1863e-01, -2.9981e-02, 5.5444e-02, 3.4524e-03,\n",
|
|||
|
" 4.7667e-02, -4.6273e-02, 5.8857e-02, 6.7802e-02, -1.0152e-03,\n",
|
|||
|
" -2.3958e-02, 2.7715e-02, -1.5837e-02, 3.0255e-02, 2.7764e-02,\n",
|
|||
|
" -8.5772e-03, -5.2482e-03, -3.4041e-02, -1.7598e-02, -2.3632e-02,\n",
|
|||
|
" -1.9890e-02, 9.4271e-03, 5.0524e-02, -3.7680e-02, 8.0661e-02,\n",
|
|||
|
" 3.0959e-02, 2.4361e-02, 2.6617e-02, -1.4361e-02, 1.9438e-02,\n",
|
|||
|
" -3.3619e-02, 3.0872e-02, 6.3582e-03, -1.4521e-03, -5.6649e-03,\n",
|
|||
|
" 6.3105e-03, 4.5886e-02, -9.0654e-02, -3.0558e-02, 1.0905e-01,\n",
|
|||
|
" -5.4971e-02, 3.0258e-02, -4.5004e-02, 4.7370e-02, 4.0472e-02,\n",
|
|||
|
" -1.0167e-03, -2.9013e-02, 6.6706e-03, 1.3320e-02, 2.7442e-02,\n",
|
|||
|
" -1.4620e-02, -3.8887e-02, -2.3700e-02, 6.3660e-02, 5.3285e-02,\n",
|
|||
|
" 9.2250e-03, 1.3351e-02, -6.2697e-02, 4.8761e-02, 6.6263e-02,\n",
|
|||
|
" -5.4074e-03, -7.5083e-02, 3.8809e-03, -3.1598e-02, -2.8409e-03,\n",
|
|||
|
" -2.3826e-02, 1.9625e-02, 4.6371e-03, -4.6053e-02, -1.1388e-02,\n",
|
|||
|
" -9.7393e-02, 2.5569e-02, 7.0440e-04, -1.6392e-02, -1.7159e-02,\n",
|
|||
|
" -1.8155e-02, 1.1288e-02, -6.0118e-02, -5.2117e-02, -2.0518e-03,\n",
|
|||
|
" 1.2293e-02, -4.8702e-02, 5.0681e-02, -7.8261e-03, -6.0259e-02,\n",
|
|||
|
" 2.5790e-02, -2.5972e-02, 1.4556e-03, 1.5539e-02, 2.5964e-02,\n",
|
|||
|
" 4.1747e-02, -4.6959e-02, 4.5961e-02, 1.2592e-02, 1.6024e-02,\n",
|
|||
|
" -7.0781e-02, 2.9707e-02, -1.9970e-02, -2.4453e-02, 1.0534e-01,\n",
|
|||
|
" -3.5986e-02, -1.1281e-02, 3.6299e-02, 7.7332e-03, -8.4582e-03,\n",
|
|||
|
" 1.0414e-01, -7.5003e-02, 1.8392e-02, 2.5007e-02, 5.3709e-05,\n",
|
|||
|
" 1.1661e-02, 5.3652e-02, -4.0520e-03, -2.6330e-02, -1.1297e-01,\n",
|
|||
|
" 2.0622e-02, -1.1506e-02, -3.9126e-02, -3.2559e-02, 1.6903e-02,\n",
|
|||
|
" -7.6179e-02, 1.0538e-01, -3.3585e-02, 2.9592e-02, -9.8638e-02,\n",
|
|||
|
" 2.3429e-01, -1.2447e-02, 4.3283e-02, -5.9157e-02, 9.1898e-02,\n",
|
|||
|
" 2.2950e-02, -5.2114e-02, -2.2760e-02, -1.0465e-02, -1.1089e-01,\n",
|
|||
|
" 2.2758e-02, 3.4435e-02, 2.1491e-02, -5.0505e-02, -1.5447e-02,\n",
|
|||
|
" -1.0494e-02, 1.6680e-02, 2.6068e-02, -9.1764e-02, -4.0106e-03,\n",
|
|||
|
" 4.8605e-02, -2.4436e-02, 1.7709e-02, 1.9984e-02, 4.5895e-02,\n",
|
|||
|
" 7.1538e-02, 6.0457e-02, -5.3648e-02, -2.2555e-02, -7.4055e-02,\n",
|
|||
|
" -3.8501e-02, -5.9608e-03, 2.8740e-02, -2.4337e-02, 1.9937e-02,\n",
|
|||
|
" 4.6275e-02, -6.1770e-02, 2.4231e-02, -5.1672e-02, 3.2617e-02,\n",
|
|||
|
" 4.1688e-02, -5.5629e-02, 4.5233e-02, 2.8302e-02, 7.1679e-02,\n",
|
|||
|
" 3.3194e-02, -3.8711e-02, 2.4011e-02, 7.0962e-02, 2.2012e-02,\n",
|
|||
|
" -2.7202e-02, 3.5133e-02, 8.8852e-03, 1.1321e-02, 5.4091e-03,\n",
|
|||
|
" 4.8605e-04, 9.4150e-03, 1.7323e-02, -2.4181e-02, -3.8130e-02,\n",
|
|||
|
" -3.6088e-02, 1.1920e-03, -4.5289e-02, -2.7036e-02, 4.3711e-02,\n",
|
|||
|
" -4.3235e-02, -9.8394e-03, -1.6918e-02, -8.2358e-02, 1.1723e-02,\n",
|
|||
|
" -2.4647e-02, -1.4637e-02, 2.5583e-02, -6.2279e-02, 5.4639e-02,\n",
|
|||
|
" 5.6699e-02, 3.7210e-02, 3.1042e-02, -6.2109e-02, -8.3125e-02,\n",
|
|||
|
" -5.3028e-02, 2.0527e-02, -2.1677e-02, -7.7717e-02, -3.5835e-02,\n",
|
|||
|
" 1.5833e-02, 3.2097e-02, -9.4851e-02, 2.1548e-02, -1.4085e-02,\n",
|
|||
|
" 1.0706e-02, 1.0919e-03, -2.1583e-02, 7.1433e-03, -5.3452e-02,\n",
|
|||
|
" -3.6993e-02, 7.3571e-02, -2.1420e-02, -2.2431e-03, -4.2342e-02,\n",
|
|||
|
" 6.1201e-02, 2.1760e-02])\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"testowe_in_lista = testowe[\"in\"].tolist()\n",
|
|||
|
"testowe_out_lista = testowe[\"out\"].tolist()\n",
|
|||
|
"\n",
|
|||
|
"print(len(testowe_in_lista))\n",
|
|||
|
"print(testowe_in_lista[0])\n",
|
|||
|
"print(testowe_out_lista[0])\n",
|
|||
|
"\n",
|
|||
|
"zanurzenia_testowe_in = zanurzenia_zdan(testowe_in_lista)\n",
|
|||
|
"zanurzenia_testowe_out = zanurzenia_zdan(testowe_out_lista)\n",
|
|||
|
"\n",
|
|||
|
"print(zanurzenia_testowe_in.shape)\n",
|
|||
|
"print(zanurzenia_testowe_in[0])\n",
|
|||
|
"print(zanurzenia_testowe_out[0])"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "ejFNMCvpzjRa"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Tensory - reprezentacje pierwszych wersów wierszy testowych."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 80,
|
|||
|
"metadata": {
|
|||
|
"id": "n5yIfk1AzjRa",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "80cd18fa-d897-48ce-f3ac-203f4f36a860"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"1490\n",
|
|||
|
"torch.Size([617])\n",
|
|||
|
"tensor([-8.0259e-02, -4.3918e-03, -7.8638e-02, -1.2816e-02, 1.5959e-02,\n",
|
|||
|
" -3.5314e-02, 1.3728e-03, 2.2271e-02, 3.7977e-02, -1.1226e-03,\n",
|
|||
|
" 3.3940e-02, -2.2293e-01, 8.9278e-03, 7.3494e-03, -2.0129e-02,\n",
|
|||
|
" 6.0954e-02, -4.4464e-02, -4.4885e-02, -5.4901e-03, -1.3792e-03,\n",
|
|||
|
" -2.6079e-02, -5.3571e-02, -5.9173e-02, -6.3475e-03, 3.8071e-02,\n",
|
|||
|
" -8.6587e-02, 7.1489e-02, 5.7802e-03, -2.7479e-02, 1.6377e-02,\n",
|
|||
|
" 1.5447e-02, 2.3025e-02, -2.7736e-02, 5.4993e-02, 3.0404e-03,\n",
|
|||
|
" 2.3296e-02, -7.4090e-04, 5.2373e-03, -1.0805e-01, -2.2483e-02,\n",
|
|||
|
" 5.9799e-03, 1.5623e-02, 1.7414e-02, -9.5345e-02, -4.4511e-02,\n",
|
|||
|
" -7.3634e-02, 2.4519e-02, 7.1082e-03, -4.7360e-03, -1.5421e-02,\n",
|
|||
|
" 2.5125e-02, 3.7092e-02, 3.7218e-02, 5.5716e-02, 3.5407e-02,\n",
|
|||
|
" 4.8798e-03, -3.7684e-02, 9.2484e-02, -4.7923e-02, 5.9173e-02,\n",
|
|||
|
" -2.9434e-02, -1.1721e-02, -9.8530e-03, -3.6479e-02, -1.6005e-03,\n",
|
|||
|
" 3.3847e-02, 1.8198e-02, 1.2230e-02, -2.5434e-02, 3.6206e-02,\n",
|
|||
|
" 1.9129e-02, -7.9656e-03, -8.5516e-03, -1.2792e-02, -5.1442e-02,\n",
|
|||
|
" -4.0518e-02, -1.0302e-02, 1.5046e-02, -5.4264e-02, -2.5881e-02,\n",
|
|||
|
" 1.8931e-02, -2.8339e-02, 7.3752e-03, 2.9828e-02, 1.9680e-02,\n",
|
|||
|
" -6.8792e-02, 1.6888e-02, 3.2706e-02, 8.8921e-02, -4.5339e-02,\n",
|
|||
|
" 6.0546e-03, -5.0379e-02, 3.0022e-02, 5.2648e-02, 6.7230e-02,\n",
|
|||
|
" 3.6637e-02, -3.5036e-02, -1.6691e-02, -3.4201e-02, -4.2494e-02,\n",
|
|||
|
" 9.5922e-02, -2.4616e-02, 5.9890e-02, 5.0630e-02, 7.1451e-03,\n",
|
|||
|
" -1.8633e-03, -3.7804e-02, -3.9666e-02, -6.1533e-02, -4.5068e-02,\n",
|
|||
|
" 1.4756e-02, 7.5407e-02, 4.4799e-02, 7.8929e-03, 7.9047e-03,\n",
|
|||
|
" -3.2292e-02, 4.2006e-02, 2.6062e-02, -1.8948e-02, -6.3121e-04,\n",
|
|||
|
" -5.5451e-02, -7.0686e-02, 7.4786e-02, -4.4551e-02, 2.0545e-02,\n",
|
|||
|
" 5.5521e-02, -4.7396e-02, -2.8430e-02, -7.7508e-03, 6.9895e-02,\n",
|
|||
|
" 3.2122e-02, -1.1592e-02, 3.1207e-03, 3.8250e-02, -4.3830e-02,\n",
|
|||
|
" -4.6368e-02, -2.7975e-02, 2.0730e-02, -6.5732e-02, 6.2236e-02,\n",
|
|||
|
" -3.4223e-02, -7.2456e-02, -6.0369e-02, 3.2526e-03, 1.6138e-02,\n",
|
|||
|
" -5.2011e-02, 8.9266e-02, 2.8281e-02, 2.6365e-02, 3.9720e-02,\n",
|
|||
|
" 2.5165e-02, 3.5209e-02, -5.6761e-02, -1.0289e-02, 4.2798e-02,\n",
|
|||
|
" -7.8977e-03, 3.4015e-02, 6.7800e-02, -8.9471e-03, -2.1570e-02,\n",
|
|||
|
" 2.5793e-02, -1.3697e-02, -2.4169e-03, -2.9232e-02, -1.3631e-02,\n",
|
|||
|
" -4.5947e-02, -6.5127e-02, -6.7566e-03, -2.4052e-02, 6.7700e-02,\n",
|
|||
|
" -1.6108e-02, -4.2147e-02, -7.6850e-02, 2.7986e-02, 1.2986e-02,\n",
|
|||
|
" -2.9518e-03, -1.5767e-02, 3.5351e-03, -2.3380e-03, 5.7797e-02,\n",
|
|||
|
" -2.9714e-02, 6.6649e-02, -2.4828e-02, 5.1280e-03, -2.2814e-02,\n",
|
|||
|
" 2.1011e-02, -4.1826e-03, -2.0886e-02, -6.4805e-02, -2.9477e-02,\n",
|
|||
|
" 4.3137e-02, 2.8640e-03, 9.4253e-03, -6.3438e-02, 1.9139e-02,\n",
|
|||
|
" 7.1025e-02, -9.1945e-02, -4.1015e-02, -1.0007e-01, 6.3948e-02,\n",
|
|||
|
" -3.6265e-02, -5.8861e-02, 4.3239e-02, 3.4465e-02, -1.0871e-02,\n",
|
|||
|
" 3.0624e-02, -3.6270e-02, 6.0533e-02, -1.2985e-03, -2.3633e-02,\n",
|
|||
|
" -7.3279e-03, 2.1527e-02, -7.2312e-03, -5.3978e-03, 3.4182e-02,\n",
|
|||
|
" 1.0214e-01, -1.1936e-03, -1.6545e-02, -4.1180e-02, -3.1017e-02,\n",
|
|||
|
" 1.5271e-02, 4.7796e-02, 4.7201e-03, -1.9967e-02, 4.6641e-02,\n",
|
|||
|
" -2.9648e-02, 5.2540e-02, -8.6410e-03, -4.8120e-02, 6.9946e-03,\n",
|
|||
|
" 1.8410e-02, 1.5676e-03, -9.5262e-02, 1.3219e-03, 1.0692e-01,\n",
|
|||
|
" 2.1499e-02, -2.3225e-02, 1.0442e-02, -1.0519e-02, -3.1683e-02,\n",
|
|||
|
" -4.6675e-02, 1.1863e-02, 2.5517e-02, -1.1842e-02, -7.3877e-02,\n",
|
|||
|
" -6.9165e-02, -1.5360e-02, -9.3084e-04, 8.0512e-02, 4.7033e-02,\n",
|
|||
|
" -1.5356e-02, -1.4746e-02, 1.1060e-02, -4.7805e-03, 2.3480e-03,\n",
|
|||
|
" -2.0961e-02, 1.9839e-02, -2.0290e-02, 7.5530e-03, 3.3894e-02,\n",
|
|||
|
" 1.0894e-02, 7.2060e-03, 3.7515e-02, 2.4427e-02, 2.4834e-02,\n",
|
|||
|
" 1.5667e-02, -5.3774e-02, 1.1743e-01, -7.5955e-02, -1.3473e-02,\n",
|
|||
|
" 4.7957e-04, 4.3490e-02, -5.8301e-02, 1.2368e-02, -2.9888e-02,\n",
|
|||
|
" 6.5488e-02, 4.7251e-02, 5.2683e-02, -6.4732e-02, 3.2558e-03,\n",
|
|||
|
" -3.6795e-03, 6.1479e-02, -9.0703e-02, 3.9305e-02, -1.0025e-02,\n",
|
|||
|
" -4.4901e-02, -1.0818e-01, -4.8924e-02, 7.9814e-02, -1.3939e-02,\n",
|
|||
|
" 3.0613e-02, -6.9328e-02, 8.5495e-02, 3.0384e-02, -6.3244e-04,\n",
|
|||
|
" -6.1677e-02, 7.2960e-02, -3.2197e-02, 3.4590e-02, 1.5042e-02,\n",
|
|||
|
" 5.0482e-02, -5.3699e-02, -4.8971e-03, -5.5674e-02, -5.8709e-02,\n",
|
|||
|
" -1.5166e-02, 2.7467e-02, 5.4554e-02, -4.0191e-02, 4.1437e-02,\n",
|
|||
|
" 8.1562e-03, 1.8709e-02, 8.0741e-02, 2.2606e-03, -2.0263e-03,\n",
|
|||
|
" -1.2661e-02, 1.7509e-03, 3.6689e-02, -1.9632e-02, 3.6983e-02,\n",
|
|||
|
" 4.8102e-02, 1.1438e-02, -7.4046e-02, -4.6997e-02, 4.7330e-02,\n",
|
|||
|
" -2.0255e-02, 6.5378e-04, -1.1966e-02, 1.8390e-02, 4.0788e-02,\n",
|
|||
|
" 1.0731e-02, -4.0035e-03, 3.8093e-02, -4.3862e-02, -4.8802e-02,\n",
|
|||
|
" -1.1096e-01, -2.4208e-02, -2.2915e-02, 8.4262e-02, 6.3164e-02,\n",
|
|||
|
" 4.9571e-02, 4.3982e-02, -5.4121e-02, 3.2768e-02, 4.9195e-03,\n",
|
|||
|
" 3.3878e-02, -2.3583e-02, 6.8083e-02, -8.3218e-03, 1.1866e-02,\n",
|
|||
|
" -1.7878e-02, -3.4026e-02, 1.6795e-02, 5.1865e-02, 7.5081e-03,\n",
|
|||
|
" -3.6319e-02, -1.7098e-02, 2.4454e-02, 4.4789e-02, 6.2583e-02,\n",
|
|||
|
" 2.1719e-02, 1.4467e-03, -1.8073e-02, -6.1717e-02, 7.7397e-02,\n",
|
|||
|
" -5.8391e-02, -2.9188e-02, 2.9497e-02, -7.1020e-02, -2.1456e-02,\n",
|
|||
|
" -2.9981e-02, -2.5142e-02, -3.5100e-02, -6.0593e-03, 5.6244e-02,\n",
|
|||
|
" -1.0404e-03, 2.5305e-02, 5.3293e-02, 1.1524e-02, 1.1573e-03,\n",
|
|||
|
" -5.5486e-02, 2.0558e-02, 1.6097e-02, -1.1003e-02, 3.2383e-02,\n",
|
|||
|
" -4.5159e-02, -2.1123e-02, 2.9454e-02, 6.9495e-02, -3.6259e-02,\n",
|
|||
|
" 2.4084e-02, -9.5467e-02, 1.4566e-03, 5.1955e-02, 2.9325e-02,\n",
|
|||
|
" -1.2771e-02, 3.1845e-02, -6.5127e-03, 2.8762e-02, -1.7184e-02,\n",
|
|||
|
" 4.1251e-02, -3.8555e-02, -3.7427e-02, -4.5343e-02, -6.2431e-02,\n",
|
|||
|
" -2.7663e-02, 2.0913e-02, 1.2883e-02, 1.0214e-02, -3.8013e-02,\n",
|
|||
|
" 2.0302e-01, -5.2109e-02, -4.6819e-02, 2.0404e-03, 1.4092e-02,\n",
|
|||
|
" 3.2596e-02, -2.1809e-02, -4.9518e-02, -9.6773e-03, -5.0380e-02,\n",
|
|||
|
" 6.1177e-03, 1.0617e-01, -1.8102e-02, -1.9206e-02, -5.2882e-02,\n",
|
|||
|
" 1.3522e-02, -1.7683e-03, 8.2799e-03, -5.8206e-02, -1.2375e-02,\n",
|
|||
|
" 2.6137e-02, 1.4741e-02, 6.6093e-02, 7.0706e-02, 1.2897e-02,\n",
|
|||
|
" 5.1019e-02, 9.4399e-03, 1.3937e-02, -3.3179e-02, 4.0461e-02,\n",
|
|||
|
" -3.1880e-02, 6.4492e-03, 6.0355e-03, -2.7819e-02, -7.4351e-02,\n",
|
|||
|
" -5.3170e-02, -7.9592e-02, -3.0133e-02, 2.7070e-02, -1.5751e-02,\n",
|
|||
|
" -4.8386e-03, -1.3120e-02, -5.7183e-02, 2.0625e-02, 3.4714e-02,\n",
|
|||
|
" 2.1150e-02, -9.5332e-02, -8.2080e-02, 2.3911e-02, 5.3298e-02,\n",
|
|||
|
" -7.3405e-02, -9.1094e-03, -3.9370e-02, 1.8139e-02, -6.0592e-02,\n",
|
|||
|
" 1.2654e-02, -1.7347e-02, -2.7792e-02, -1.4431e-02, 1.7151e-02,\n",
|
|||
|
" 2.8065e-02, -3.6322e-02, -2.1867e-02, 1.4305e-03, 3.6528e-02,\n",
|
|||
|
" 2.5914e-02, 1.9917e-02, -5.9702e-02, -5.0098e-02, -5.6331e-03,\n",
|
|||
|
" -6.3057e-02, -1.2070e-02, -1.6831e-02, -2.5532e-02, 8.5733e-02,\n",
|
|||
|
" 3.7801e-02, 2.8635e-02, 5.3134e-02, -6.1810e-02, -4.4343e-02,\n",
|
|||
|
" -6.9733e-02, -2.0240e-03, 7.8664e-03, -3.0743e-03, -1.5851e-03,\n",
|
|||
|
" 8.3178e-03, 4.4983e-02, -5.7173e-02, -3.7285e-02, 3.5458e-03,\n",
|
|||
|
" 9.0360e-02, -5.2260e-02, 2.6192e-02, 2.7662e-02, -9.6391e-03,\n",
|
|||
|
" -1.2950e-02, 1.0944e-01, 4.4497e-02, -4.9483e-02, -9.2012e-02,\n",
|
|||
|
" 2.9951e-02, -2.5255e-02, 8.0000e+00, 2.2000e+01, 1.2000e+01,\n",
|
|||
|
" 7.0000e+00, 3.0000e+00, 9.0000e+00, 2.1000e+01, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 2.5000e+01, 1.2000e+01, 2.1000e+01,\n",
|
|||
|
" 3.4000e+01, 1.0000e+00, 2.0000e+00, 1.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 2.0000e+00, 3.0000e+00, 4.0000e+00,\n",
|
|||
|
" 2.0000e+00, 2.0000e+00, 3.0000e+00, 4.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00])\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"x_test = []\n",
|
|||
|
"for indeks_wersu_pierwszego in range(len(zanurzenia_testowe_in)):\n",
|
|||
|
" x_test.append(torch.cat(\n",
|
|||
|
" (\n",
|
|||
|
" zanurzenia_testowe_in[indeks_wersu_pierwszego],\n",
|
|||
|
" torch.from_numpy(poczatki_testowe_in[indeks_wersu_pierwszego]),\n",
|
|||
|
" torch.from_numpy(konce_testowe_in[indeks_wersu_pierwszego]),\n",
|
|||
|
" torch.from_numpy(tony_testowe_in[indeks_wersu_pierwszego])\n",
|
|||
|
" )\n",
|
|||
|
" ))\n",
|
|||
|
"print(len(x_test))\n",
|
|||
|
"print(x_test[0].shape)\n",
|
|||
|
"print(x_test[0])"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "_oiP9xtvzjRb"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Tensory - reprezentacje drugich wersów wierszy testowych."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 81,
|
|||
|
"metadata": {
|
|||
|
"id": "gGPmfEYrzjRb",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "7f73579c-ffb9-4b51-88b7-b1a3cab26dd1"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"1490\n",
|
|||
|
"torch.Size([617])\n",
|
|||
|
"tensor([-4.2662e-02, -3.1708e-02, -5.6512e-02, 4.0084e-02, -3.8748e-02,\n",
|
|||
|
" 9.6097e-03, 4.7008e-02, 7.5668e-02, -1.3552e-02, 1.1450e-02,\n",
|
|||
|
" 7.6131e-02, -2.4726e-01, -2.2119e-02, -3.7263e-02, -1.6699e-03,\n",
|
|||
|
" -2.8011e-03, -7.1475e-03, 1.0944e-02, 1.0504e-02, -1.9638e-02,\n",
|
|||
|
" 3.1485e-02, -6.9084e-03, -2.0084e-02, -2.8139e-02, -2.1698e-02,\n",
|
|||
|
" -4.5725e-02, 6.2876e-02, -2.4744e-02, -5.4077e-02, 1.1777e-02,\n",
|
|||
|
" 3.2030e-02, -6.9665e-03, 1.5294e-02, -8.0923e-02, 2.8682e-02,\n",
|
|||
|
" 6.8040e-03, -3.3103e-03, 2.6560e-02, -6.0841e-02, 2.1151e-03,\n",
|
|||
|
" -3.2793e-02, 1.4114e-02, -3.0558e-02, -6.7261e-02, -7.8080e-02,\n",
|
|||
|
" -5.6005e-02, -2.4466e-02, 3.1131e-02, 1.2249e-02, -2.6226e-02,\n",
|
|||
|
" 5.6157e-03, -1.4219e-02, 6.1117e-02, 8.7854e-02, 3.2294e-02,\n",
|
|||
|
" -7.5786e-02, -3.8687e-03, 5.8290e-02, -1.8115e-02, 9.2157e-03,\n",
|
|||
|
" -1.9775e-02, -1.4765e-02, -8.1864e-02, -5.2147e-02, -3.3906e-02,\n",
|
|||
|
" 4.7297e-02, 1.7286e-02, -3.8963e-02, -7.2971e-03, 4.6875e-02,\n",
|
|||
|
" -7.3317e-02, 2.3185e-02, -1.0216e-02, -4.6695e-02, -1.0624e-02,\n",
|
|||
|
" -8.6403e-02, 1.0679e-02, 3.8384e-02, -2.0390e-02, -4.8561e-02,\n",
|
|||
|
" 1.7503e-02, -2.4822e-02, -1.0118e-01, -1.1536e-02, 1.2367e-02,\n",
|
|||
|
" -2.8817e-02, 3.8065e-02, -4.8299e-03, 3.6943e-02, -8.7723e-03,\n",
|
|||
|
" -3.3175e-02, -2.9473e-02, -1.4506e-02, 4.3669e-02, -1.3243e-02,\n",
|
|||
|
" 9.1637e-03, -5.2170e-02, 6.5091e-02, -6.0895e-03, -8.3091e-02,\n",
|
|||
|
" 4.1754e-03, -8.1591e-03, 1.3681e-02, 4.9336e-02, 5.1179e-02,\n",
|
|||
|
" 3.0331e-02, 8.2378e-03, 3.4116e-03, 8.7714e-03, 2.6082e-02,\n",
|
|||
|
" -5.9453e-02, 5.4098e-02, 1.5604e-02, 6.1685e-02, -6.5803e-02,\n",
|
|||
|
" -4.3957e-03, 2.8998e-02, 7.4083e-02, -7.5646e-02, 9.9494e-03,\n",
|
|||
|
" -3.0923e-02, -4.0035e-03, 2.6690e-02, -1.7936e-02, 5.9101e-02,\n",
|
|||
|
" -2.6199e-02, -8.9598e-03, -3.3770e-02, 2.5304e-02, -2.2467e-02,\n",
|
|||
|
" -8.5894e-03, -6.5153e-02, -1.8516e-02, -3.2236e-02, -6.9214e-02,\n",
|
|||
|
" -6.8273e-02, -6.8416e-03, 3.0168e-02, 2.6452e-02, 2.7235e-02,\n",
|
|||
|
" -1.4605e-02, 4.0644e-02, -3.1382e-02, -6.6872e-02, 3.6762e-02,\n",
|
|||
|
" 3.3090e-02, 4.1689e-02, 6.2443e-02, 1.1023e-01, 4.5009e-02,\n",
|
|||
|
" -5.9660e-02, -1.9799e-02, -2.1019e-02, -1.4585e-02, -4.9002e-02,\n",
|
|||
|
" 6.7098e-03, 4.8637e-02, 2.6957e-02, -1.2555e-01, -4.4153e-02,\n",
|
|||
|
" -3.7129e-02, -3.5815e-03, 3.7462e-03, -3.9305e-02, 6.6185e-03,\n",
|
|||
|
" -5.7863e-03, -4.5151e-02, 5.8374e-02, -7.9883e-02, 5.8874e-02,\n",
|
|||
|
" 2.9350e-02, -3.0508e-02, -5.7538e-02, 1.4630e-02, 1.7199e-02,\n",
|
|||
|
" -4.2483e-02, -6.1783e-02, 1.8635e-02, -5.8922e-03, 3.2036e-02,\n",
|
|||
|
" 1.1720e-02, 2.9453e-02, -4.1426e-02, -1.3919e-04, 7.4231e-02,\n",
|
|||
|
" 4.0965e-02, 5.2020e-02, -8.5824e-03, -5.0865e-02, 3.3741e-02,\n",
|
|||
|
" 3.1936e-02, -2.8163e-02, 2.5174e-02, -6.6295e-03, 5.4489e-03,\n",
|
|||
|
" 4.8801e-02, -3.4366e-02, 1.5345e-02, -4.9533e-02, 4.1175e-02,\n",
|
|||
|
" 1.0052e-02, -8.0770e-03, 1.6990e-03, 2.7498e-02, -2.0195e-02,\n",
|
|||
|
" 7.3058e-02, -1.3125e-02, 5.5075e-02, 1.0222e-02, -6.1044e-02,\n",
|
|||
|
" 1.6483e-02, 1.7518e-02, -3.5582e-02, 6.5297e-03, 4.8142e-02,\n",
|
|||
|
" -9.1526e-03, -2.5292e-02, -9.2555e-02, -2.7960e-02, -6.7617e-02,\n",
|
|||
|
" -2.7919e-02, 2.5797e-02, -3.3792e-02, -1.7559e-02, 5.0696e-03,\n",
|
|||
|
" 1.9502e-02, 9.8111e-02, -3.5467e-02, -7.8175e-03, -1.3069e-02,\n",
|
|||
|
" 1.1869e-02, -1.2426e-02, -3.8581e-02, 1.3946e-02, 8.5697e-02,\n",
|
|||
|
" 5.9456e-03, -7.0115e-03, 3.2226e-03, 3.0390e-03, -5.4671e-03,\n",
|
|||
|
" -4.7213e-02, 3.5392e-02, -1.5239e-03, 7.3812e-02, -1.7222e-02,\n",
|
|||
|
" -1.2772e-03, 2.1770e-02, -4.6258e-02, 3.5956e-02, 2.6292e-02,\n",
|
|||
|
" 5.5864e-03, 3.9837e-03, 1.4386e-02, -3.0420e-03, 1.6326e-02,\n",
|
|||
|
" -4.3116e-02, 6.0202e-04, -6.6577e-02, 4.4794e-02, -6.0236e-02,\n",
|
|||
|
" 3.3927e-03, 9.1377e-02, 6.7364e-02, -1.6208e-02, -4.1574e-02,\n",
|
|||
|
" 3.2682e-02, 7.2001e-03, 2.8854e-02, -2.5179e-02, -4.5137e-02,\n",
|
|||
|
" 5.7746e-03, 3.8036e-02, -1.8377e-02, 5.4035e-02, 1.5138e-02,\n",
|
|||
|
" 4.5383e-02, 1.1084e-02, -2.0204e-02, -1.0160e-02, 1.8784e-02,\n",
|
|||
|
" 9.1744e-03, 6.3865e-02, -6.9441e-02, -3.0443e-02, 1.0225e-02,\n",
|
|||
|
" 9.9409e-03, -1.1863e-01, -2.9981e-02, 5.5444e-02, 3.4524e-03,\n",
|
|||
|
" 4.7667e-02, -4.6273e-02, 5.8857e-02, 6.7802e-02, -1.0152e-03,\n",
|
|||
|
" -2.3958e-02, 2.7715e-02, -1.5837e-02, 3.0255e-02, 2.7764e-02,\n",
|
|||
|
" -8.5772e-03, -5.2482e-03, -3.4041e-02, -1.7598e-02, -2.3632e-02,\n",
|
|||
|
" -1.9890e-02, 9.4271e-03, 5.0524e-02, -3.7680e-02, 8.0661e-02,\n",
|
|||
|
" 3.0959e-02, 2.4361e-02, 2.6617e-02, -1.4361e-02, 1.9438e-02,\n",
|
|||
|
" -3.3619e-02, 3.0872e-02, 6.3582e-03, -1.4521e-03, -5.6649e-03,\n",
|
|||
|
" 6.3105e-03, 4.5886e-02, -9.0654e-02, -3.0558e-02, 1.0905e-01,\n",
|
|||
|
" -5.4971e-02, 3.0258e-02, -4.5004e-02, 4.7370e-02, 4.0472e-02,\n",
|
|||
|
" -1.0167e-03, -2.9013e-02, 6.6706e-03, 1.3320e-02, 2.7442e-02,\n",
|
|||
|
" -1.4620e-02, -3.8887e-02, -2.3700e-02, 6.3660e-02, 5.3285e-02,\n",
|
|||
|
" 9.2250e-03, 1.3351e-02, -6.2697e-02, 4.8761e-02, 6.6263e-02,\n",
|
|||
|
" -5.4074e-03, -7.5083e-02, 3.8809e-03, -3.1598e-02, -2.8409e-03,\n",
|
|||
|
" -2.3826e-02, 1.9625e-02, 4.6371e-03, -4.6053e-02, -1.1388e-02,\n",
|
|||
|
" -9.7393e-02, 2.5569e-02, 7.0440e-04, -1.6392e-02, -1.7159e-02,\n",
|
|||
|
" -1.8155e-02, 1.1288e-02, -6.0118e-02, -5.2117e-02, -2.0518e-03,\n",
|
|||
|
" 1.2293e-02, -4.8702e-02, 5.0681e-02, -7.8261e-03, -6.0259e-02,\n",
|
|||
|
" 2.5790e-02, -2.5972e-02, 1.4556e-03, 1.5539e-02, 2.5964e-02,\n",
|
|||
|
" 4.1747e-02, -4.6959e-02, 4.5961e-02, 1.2592e-02, 1.6024e-02,\n",
|
|||
|
" -7.0781e-02, 2.9707e-02, -1.9970e-02, -2.4453e-02, 1.0534e-01,\n",
|
|||
|
" -3.5986e-02, -1.1281e-02, 3.6299e-02, 7.7332e-03, -8.4582e-03,\n",
|
|||
|
" 1.0414e-01, -7.5003e-02, 1.8392e-02, 2.5007e-02, 5.3709e-05,\n",
|
|||
|
" 1.1661e-02, 5.3652e-02, -4.0520e-03, -2.6330e-02, -1.1297e-01,\n",
|
|||
|
" 2.0622e-02, -1.1506e-02, -3.9126e-02, -3.2559e-02, 1.6903e-02,\n",
|
|||
|
" -7.6179e-02, 1.0538e-01, -3.3585e-02, 2.9592e-02, -9.8638e-02,\n",
|
|||
|
" 2.3429e-01, -1.2447e-02, 4.3283e-02, -5.9157e-02, 9.1898e-02,\n",
|
|||
|
" 2.2950e-02, -5.2114e-02, -2.2760e-02, -1.0465e-02, -1.1089e-01,\n",
|
|||
|
" 2.2758e-02, 3.4435e-02, 2.1491e-02, -5.0505e-02, -1.5447e-02,\n",
|
|||
|
" -1.0494e-02, 1.6680e-02, 2.6068e-02, -9.1764e-02, -4.0106e-03,\n",
|
|||
|
" 4.8605e-02, -2.4436e-02, 1.7709e-02, 1.9984e-02, 4.5895e-02,\n",
|
|||
|
" 7.1538e-02, 6.0457e-02, -5.3648e-02, -2.2555e-02, -7.4055e-02,\n",
|
|||
|
" -3.8501e-02, -5.9608e-03, 2.8740e-02, -2.4337e-02, 1.9937e-02,\n",
|
|||
|
" 4.6275e-02, -6.1770e-02, 2.4231e-02, -5.1672e-02, 3.2617e-02,\n",
|
|||
|
" 4.1688e-02, -5.5629e-02, 4.5233e-02, 2.8302e-02, 7.1679e-02,\n",
|
|||
|
" 3.3194e-02, -3.8711e-02, 2.4011e-02, 7.0962e-02, 2.2012e-02,\n",
|
|||
|
" -2.7202e-02, 3.5133e-02, 8.8852e-03, 1.1321e-02, 5.4091e-03,\n",
|
|||
|
" 4.8605e-04, 9.4150e-03, 1.7323e-02, -2.4181e-02, -3.8130e-02,\n",
|
|||
|
" -3.6088e-02, 1.1920e-03, -4.5289e-02, -2.7036e-02, 4.3711e-02,\n",
|
|||
|
" -4.3235e-02, -9.8394e-03, -1.6918e-02, -8.2358e-02, 1.1723e-02,\n",
|
|||
|
" -2.4647e-02, -1.4637e-02, 2.5583e-02, -6.2279e-02, 5.4639e-02,\n",
|
|||
|
" 5.6699e-02, 3.7210e-02, 3.1042e-02, -6.2109e-02, -8.3125e-02,\n",
|
|||
|
" -5.3028e-02, 2.0527e-02, -2.1677e-02, -7.7717e-02, -3.5835e-02,\n",
|
|||
|
" 1.5833e-02, 3.2097e-02, -9.4851e-02, 2.1548e-02, -1.4085e-02,\n",
|
|||
|
" 1.0706e-02, 1.0919e-03, -2.1583e-02, 7.1433e-03, -5.3452e-02,\n",
|
|||
|
" -3.6993e-02, 7.3571e-02, -2.1420e-02, -2.2431e-03, -4.2342e-02,\n",
|
|||
|
" 6.1201e-02, 2.1760e-02, 1.6000e+01, 2.2000e+01, 2.2000e+01,\n",
|
|||
|
" 2.2000e+01, 2.2000e+01, 1.3000e+01, 1.7000e+01, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 2.4000e+01, 2.1000e+01, 1.0000e+00,\n",
|
|||
|
" 2.3000e+01, 2.5000e+01, 3.2000e+01, 2.0000e+01, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 2.0000e+00, 1.0000e+00, 3.0000e+00,\n",
|
|||
|
" 3.0000e+00, 3.0000e+00, 1.0000e+00, 1.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
|
|||
|
" 0.0000e+00, 0.0000e+00])\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"y_test = []\n",
|
|||
|
"for indeks_wersu_pierwszego in range(len(zanurzenia_testowe_out)):\n",
|
|||
|
" y_test.append(\n",
|
|||
|
" torch.cat(\n",
|
|||
|
" (\n",
|
|||
|
" zanurzenia_testowe_out[indeks_wersu_pierwszego],\n",
|
|||
|
" torch.from_numpy(poczatki_testowe_out[indeks_wersu_pierwszego]),\n",
|
|||
|
" torch.from_numpy(konce_testowe_out[indeks_wersu_pierwszego]),\n",
|
|||
|
" torch.from_numpy(tony_testowe_out[indeks_wersu_pierwszego])\n",
|
|||
|
" )\n",
|
|||
|
" )\n",
|
|||
|
" )\n",
|
|||
|
"print(len(y_test))\n",
|
|||
|
"print(y_test[0].shape)\n",
|
|||
|
"print(y_test[0])"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "asOOP5aQzjRi"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"## Wejście do sieci neuronowej."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 82,
|
|||
|
"metadata": {
|
|||
|
"id": "WvXXbbTHzjRi",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "27087789-3a48-43c9-d624-06a13e395de0"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"4470\n",
|
|||
|
"tensor([-0.0803, -0.0044, -0.0786, ..., 0.0000, 0.0000, 0.0000])\n",
|
|||
|
"4470\n",
|
|||
|
"1\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"X_test = []\n",
|
|||
|
"Y_test = []\n",
|
|||
|
"for indeks_wersu_drugiego in range(len(x_test)):\n",
|
|||
|
" indeksy = sample(range(len(y_test)), 3)\n",
|
|||
|
" if indeks_wersu_drugiego not in indeksy:\n",
|
|||
|
" indeksy[0] = indeks_wersu_drugiego\n",
|
|||
|
" for k in indeksy:\n",
|
|||
|
" X_test.append(\n",
|
|||
|
" torch.cat(\n",
|
|||
|
" (x_test[indeks_wersu_drugiego], y_test[k])\n",
|
|||
|
" )\n",
|
|||
|
" )\n",
|
|||
|
" if indeks_wersu_drugiego==k:\n",
|
|||
|
" Y_test.append(1)\n",
|
|||
|
" else:\n",
|
|||
|
" Y_test.append(0)\n",
|
|||
|
"\n",
|
|||
|
"print(len(X_test))\n",
|
|||
|
"print(X_test[0])\n",
|
|||
|
"print(len(Y_test))\n",
|
|||
|
"print(Y_test[0])"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "UEkmFpHMzjRi"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"## Przewidywania sieci neuronowych."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 83,
|
|||
|
"metadata": {
|
|||
|
"id": "zP-1RKPIzjRj"
|
|||
|
},
|
|||
|
"outputs": [],
|
|||
|
"source": [
|
|||
|
"przewidywania_klasyfikatora = klasyfikator.predict(X_test)"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 84,
|
|||
|
"metadata": {
|
|||
|
"id": "F0Bg-q4OzjRj",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "48415441-cc80-46e6-bf80-84dfe0f40e31"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"-1.250942997983307 2.1043229513866173 0.33757049502639574 0.28136695410859397\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"przewidywania_regresora = regresor.predict(X_test)\n",
|
|||
|
"print(numpy.min(przewidywania_regresora),numpy.max(przewidywania_regresora),numpy.mean(przewidywania_regresora),numpy.median(przewidywania_regresora))"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "hW9w6QKqzjRj"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Dokładność na przygotowanych danych testowych."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 85,
|
|||
|
"metadata": {
|
|||
|
"id": "iopWADz3zjRj",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "6f46aa1e-ceea-49d1-bddb-d3db86e01ae7"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"0.8402684563758389\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"### MLPClassifier\n",
|
|||
|
"\n",
|
|||
|
"licznik = 0\n",
|
|||
|
"mianownik = 0\n",
|
|||
|
"for indeks_wersu_pierwszego in range(len(przewidywania_klasyfikatora)):\n",
|
|||
|
" mianownik+=1\n",
|
|||
|
" if przewidywania_klasyfikatora[indeks_wersu_pierwszego]==Y_test[indeks_wersu_pierwszego]:\n",
|
|||
|
" licznik+=1\n",
|
|||
|
"\n",
|
|||
|
"print(licznik/mianownik*1.0)"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 86,
|
|||
|
"metadata": {
|
|||
|
"id": "vKc1aNE7zjRk",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "efac6e23-133d-41d6-a677-2d12a1750ebd"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"0.8217002237136465\n",
|
|||
|
"0.7507829977628635\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"### MLPRegressor\n",
|
|||
|
"\n",
|
|||
|
"# Dopasowanie powyżej 0.5\n",
|
|||
|
"licznik = 0\n",
|
|||
|
"mianownik = 0\n",
|
|||
|
"for indeks_wersu_pierwszego in range(len(przewidywania_regresora)):\n",
|
|||
|
" mianownik+=1\n",
|
|||
|
" if Y_test[indeks_wersu_pierwszego]==1 and przewidywania_regresora[indeks_wersu_pierwszego]>0.5:\n",
|
|||
|
" licznik+=1\n",
|
|||
|
" elif Y_test[indeks_wersu_pierwszego]==0 and przewidywania_regresora[indeks_wersu_pierwszego]<0.5:\n",
|
|||
|
" licznik+=1\n",
|
|||
|
"\n",
|
|||
|
"print(licznik/mianownik*1.0)\n",
|
|||
|
"\n",
|
|||
|
"#Dopasowanie powyżej 0.9\n",
|
|||
|
"licznik = 0\n",
|
|||
|
"mianownik = 0\n",
|
|||
|
"for indeks_wersu_pierwszego in range(len(przewidywania_regresora)):\n",
|
|||
|
" mianownik+=1\n",
|
|||
|
" if Y_test[indeks_wersu_pierwszego]==1 and przewidywania_regresora[indeks_wersu_pierwszego]>0.9:\n",
|
|||
|
" licznik+=1\n",
|
|||
|
" elif Y_test[indeks_wersu_pierwszego]==0 and przewidywania_regresora[indeks_wersu_pierwszego]<0.9:\n",
|
|||
|
" licznik+=1\n",
|
|||
|
"\n",
|
|||
|
"print(licznik/mianownik*1.0)"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "MTJElr3JzjRk"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"## Metryka oceniająca proponowanie przez model drugiego wersu.\n",
|
|||
|
"### Jeżeli wśród propozycji nie ma spodziewanego poprawnego wersu, metryka przyjmuje minimalną wartość 0,0.\n",
|
|||
|
"### Im mniej błędnych propozycji , tym wyższy wynik metryki.\n",
|
|||
|
"### Jeżeli model proponuje tylko jeden wers i jest on poprawny, metryka przyjmuje maksymalną wartość 1,0."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 87,
|
|||
|
"metadata": {
|
|||
|
"id": "6IzT5R60zjRk"
|
|||
|
},
|
|||
|
"outputs": [],
|
|||
|
"source": [
|
|||
|
"def jagosz_score(spodziewany_wers,proponowane_wersy):\n",
|
|||
|
" if spodziewany_wers in proponowane_wersy:\n",
|
|||
|
" licznik = 1\n",
|
|||
|
" else:\n",
|
|||
|
" licznik = 0\n",
|
|||
|
" mianownik = len(proponowane_wersy)\n",
|
|||
|
" if mianownik==0:\n",
|
|||
|
" mianownik=1\n",
|
|||
|
" return licznik/mianownik*1.0"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "xV6cMa6YzjRl"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Wersja metryki dla całego zbioru."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 88,
|
|||
|
"metadata": {
|
|||
|
"id": "URvcE9FizjRl"
|
|||
|
},
|
|||
|
"outputs": [],
|
|||
|
"source": [
|
|||
|
"def jagosz_score_dla_zbioru(krotki):\n",
|
|||
|
" licznik = 0\n",
|
|||
|
" mianownik = 0\n",
|
|||
|
"\n",
|
|||
|
" for k in krotki:\n",
|
|||
|
" spodziewany_wers = k[0]\n",
|
|||
|
" proponowane_wersy = k[1]\n",
|
|||
|
" if spodziewany_wers in proponowane_wersy:\n",
|
|||
|
" licznik += 1\n",
|
|||
|
" mianownik += len(proponowane_wersy)\n",
|
|||
|
"\n",
|
|||
|
" if mianownik==0:\n",
|
|||
|
" return 0\n",
|
|||
|
" else:\n",
|
|||
|
" return licznik/mianownik*1.0"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 89,
|
|||
|
"metadata": {
|
|||
|
"id": "xVabrmx_zjRl"
|
|||
|
},
|
|||
|
"outputs": [],
|
|||
|
"source": [
|
|||
|
"wybrane_dane_testowe = sample(range(len(x_test)),10)"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "S_ZW8fCpzjRl"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"## MLPClassifier\n",
|
|||
|
"### Proponuje wszystkie wersy, dla których ocena modelu to 1."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 90,
|
|||
|
"metadata": {
|
|||
|
"id": "tW-p5cYmzjRl",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "a84be316-bb04-415e-f51d-8b9ce1cf91ed"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"wers pierwszy:\t\t 花好月圆涵画意\n",
|
|||
|
"poprawny wers drugi:\t 年丰人寿沁诗声\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['谁将片语问何求', '他乡月好俺思亲', '三杯白酒乐成仙', '峰平径长难藏景', '海深寻秘展雄才', '青山醉向一樽横', '好同蝉窟映三潭', '万般幻态杳随风', '须先百忍学张公', '珠帘难掩月多情', '莺燕对舞艳阳天', '青山四面纳千流', '一心二用两头空', '闲庭信步哼欢歌', '雾里青山画屏开', '殷殷老叶护花红', '偶观雨燕栖寒檐', '满头霜雪和新梅', '居身常抱玉壶清', '竹林满山景隽幽', '一腔热血死难消', '清风两袖带回家', '日月同辉光景嫣', '三阳开泰颂廉明', '小楼吹砌玉生寒', '锦葵昂面为迎光', '浮沉历尽许由谁', '闹中取静看擂台', '早将秋韵入诗怀', '满身花影倩人扶', '千杯浊酒醉恒长', '心牵雨骤夜归人', '与君同作太平人', '青灯久作故人看', '猴腾广宇绽琼花', '好留明月九千秋', '花狎春云露搅和', '三联书韵醉今生', '风临荷盏窃清香', '花好月圆夜长明', '河清海晏让人迷', '心宽纳海老夫能', '扬鞭跃马马行空', '鹰翔蓝宇戏搏云', '甘棠播爱岁流金', '胸中消尽是非心', '弄潮帆影港城新', '英年奋进惜时光', '堤前柳浪露春光', '故人书自日边来', '文联叶问斩妖魔', '梅香葱岭缀长虹', '万般气象壮龙年', '催开玉蕊艳无边', '清塘浴月鹤逐风', '卷帘烧烛看梅花', '圆缺朗月也浮名', '思亲情愫贯通篇', '梧桐叶上得秋声', '一杯浊酒两篇诗', '动动脑筋动静无', '新朋正续进行时', '一指清凉尽染秋', '日移松影过禅床', '无田有业不为贫', '一溪柳绿到谁家', '风临柳榭露春心', '西湖乡梦约谁寻', '远山终日送余霞', '纵情狂乱毁根基', '笛声浅扣暗推窗', '一川杨柳笼和风', '洗出芙蓉九点青', '半空摇晃寻常仁', '追求亮丽美人图', '雪融春到春融雪', '常将劲节负秋霜', '国持德政著宏篇', '千般爱意眼中留', '绿水卧听新月明', '兰心未老梦如初', '英雄力困也求人', '且由明月洗尘心', '夜灯勤礼塔中仙', '赋浓夏盛寓秋实', '翠柳清风伴杏娇', '蛇听燕语颂春光', '四方称霸一魔方', '相思一点老了谁', '花间酌酒赏蝶飞', '磋砣无奈怨摽梅', '清泉有趣自通融', '山长水远恨重重', '游春岂料梦成真', '年丰人寿沁诗声', '浮舟水面尽飞花', '捉刀李白斩斯文', '三令五申还有贪', '霜飞两鬓孔明灯', '半帘秋梦鸟也酥', '得心应手手头宽', '子孙常读未烧书', '官居宰相望王侯', '元兴世盛展宏图', '长将远景引天边', '何堪永夜漏更寒', '春风惠我也惠人', '扁舟轻荡水云长', '常教翰墨作鼓吹', '荷描夏画日钤章', '澄天月隐星今宵', '小桃几树鸟啼红', '皇城玉阙夕阳斜', '花贴幼子悦童心', '轻舟破浪过千山', '这边环境安宁', '修身松竹有高风', '一言九鼎定心神', '文庙弦音奏凯频', '晓霞含愁看早梅', '风吹杨柳翠还柔', '一丛老竹梦于胸', '时临峻岭采浮云', '观光农业载风情', '钻杆穿地唱欢歌', '松风竹韵多抒情', '掷笔从戎壮士名', '吹牛煮海火收兵', '梅花傲雪迓长春', '出门去白面书生', '无边相思似流云', '金声玉振展奇才', '一般落魄一般人', '黄金灿灿冷如冰', '春深似海梦无痕', '文中已现老成心', '何堪心乱雨难读', '石泉流水洗椰瓢', '吕布吕蒙常用兵', '无休往日浩如烟', '七年对友队尤宏', '相思不减病扶墙', '金龙对舞戏中来', '水城画卷展宏图', '三春经纬织民图', '江心美景湛空明', '蔼峰亦寄诗仙情', '江山忧患老英雄', '收篇难阻浪涛狂', '黄粱入梦悟尘心', '火牛曾胜敌千军', '沉年古木韵临风', '风流绝壁写春秋', '山窗月透一痕青', '千篇一律竞同声', '谁到篱前问姓名', '闲聊岁月万年篇', '义常若水润人心', '廉风动地畅山河', '半坡翠竹耸蓝天', '一天飞絮舞春风', '帆连水色接天涯', '须从<E9A1BB>
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: True\n",
|
|||
|
"liczba proponowanych wersów: 236\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.00423728813559322\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 人家不必论贫富\n",
|
|||
|
"poprawny wers drugi:\t 唯有读书声最佳\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['人如无欲意何求', '举帜遵章共展才', '岩上青藤攀壁升', '济助家乡晃美名', '草内多藏五步蛇', '把酒依然大丈夫', '病入膏肓有治疗', '火山光灿地遭殃', '毅力凝成跨海桥', '后乐先忧范弟昆', '疑是瑶台月下逢', '鼓瑟还从曲里来', '雀跃鱼翔谐乐多', '一心二用两头空', '悦己悦人悦世间', '木讷的人难启迪', '含露芙蓉醉海棠', '酒醉不如伴月眠', '草木逢春年年生', '画就雾云笔墨香', '悔被浮名牵累多', '笔盖古今三千年', '克俭尚勤播誉名', '闹中取静看擂台', '庖丁自有解牛刀', '规矩者应晓方圆', '言少言多尽美谈', '璋玉无瑕耀祥光', '花好月圆夜长明', '国泰民安幸福多', '然乎者一字乾坤', '沸沸扬扬世界杯', '慧智还从实践来', '电力惠民百业兴', '又何必三日闻香', '千树争高有健才', '学问无穷博古今', '富裕安康福万家', '国运弥盛史弥远', '时急方须济世才', '公正不阿辩是非', '国展宏图烈士欣', '笔点涟漪见水平', '弱不禁风是书生', '玉笛金弦赞独生', '怪句洞开谜底联', '不叫俗尘污本真', '喜报频传战士家', '日移松影过禅床', '小小儿郎立路中', '意气风发马晓春', '无田有业不为贫', '涛落沙新畔易留', '听竹尤增几许清', '大丈夫能屈能伸', '武夷阳羡品俱馨', '莫指云山认故乡', '韧节有意杜虚名', '洗出芙蓉九点青', '落日也将暮色描', '潋滟江波扑簌风', '人且清心同步行', '名享三奇显祖公', '国持德政著宏篇', '世间最难得弟兄', '枝上空吹故国风', '笑问书生君是谁', '赋浓夏盛寓秋实', '往事依然笔底新', '小觑浮名对酒歌', '早出晚归皆自然', '却为心肝伤脑筋', '达业欣成万户楼', '重义轻财德道深', '道士身怀童子功', '热血沸腾意若何', '户内美色呈辉', '宝地佛临济世人', '万里山光收画屏', '防不胜防贼近身', '知耻明荣胸臆宽', '映月迎风不肯行', '笔墨书空自忘机', '常想旁通不对头', '一意孤行不回头', '路远始于跬步间', '江水源源发电来', '关羽无停觅长兄', '小可参禅入几分', '九世同居号义门', '处世无方只守诚', '文庙弦音奏凯频', '马舞龙韵续华章', '关外又开一朵奇葩', '大势所趋水如蓝', '白发无情忽上头', '掷笔从戎壮士名', '夫再礼让妻再争', '绝代佳人不入俗', '剌史同游忆月明', '玉液溶溶滴露来', '骨头坚硬好八连', '黄金灿灿冷如冰', '腹有奸谋即兽心', '德作福田三世修', '吕布吕蒙常用兵', '百年盟约好时光', '杜曲幸有桑麻田', '竹下新笋一色鲜', '俯首甘为孺子牛', '火牛曾胜敌千军', '圣子甘心为罪囚', '小弟欢吟蜀语联', '出口频频亿万吨', '一旦出名人气高', '联内音声欠古风', '中散孤高故不凡', '谁到篱前问姓名', '马上蓝天宇拓宽', '山水相依诗易描', '美丽季节万里春', '部长铁男不定还', '心若非良必惹悲', '慷慨悲歌魏晋风', '傲物诗文有劲风', '无主孤魂百姓怜', '死后欣然上八仙', '竹叶入唇醉耋龄', '白面书生尽奶油', '岁变难更意里人', '深感人情冷似冰', '刻炬成诗韵可观', '早上欣逢笔底兄', '炎黄子孙志超群', '古韵古风誉古今', '两制宏谋百代功', '处世何须带伪装', '杉木果林桃李荣', '日照山东处处蓬莱', '菊花从此不须开', '华夏农民开喜镰', '变易何难志士心', '离合不损月分明', '古木生芽不是春', '老虎苍蝇一起除', '暑气无声入隐溪', '落草潜伏十字坡', '御世今惟不动尊', '是大英雄自虚怀', '竹色四时也不移', '美色必将随后衰', '世事浮云感慨多', '万里清风驻洁江', '开卷细同贤者谋', '积德途中永不停', '安心是药更无方', '水畔青田走马牛', '雅意如茶自在闲', '吉日迎亲有贵人', '必须意识玩绵拳', '归家且遂十年心', '诚信经营财路宽', '<27>
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: False\n",
|
|||
|
"liczba proponowanych wersów: 172\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.0\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 岳麓求知精通科技千秋重\n",
|
|||
|
"poprawny wers drugi:\t 湖湘原道感悟人生万世雄\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['瑶花雅洁馨香海国铸精神', '双龙闹海茫茫大海我争雄', '龙传人赞华夏名镇迎东风', '慈心抒自在手慈眼慈甘露慈', '贤也圣也慢步详窥玉洞云', '羊羔跪乳乌鸦哺母且思恩', '年年端午擂鼓划舟夺锦标', '情歌依旧仍随秋水染夕阳', '缘善维贤续得源流此一方', '休闲有乐掌中点击地球村', '千师作赋笔下新村韵有余', '织天织地织出人间一个家', '冰天雪地寒鱼破镜钓江翁', '瑶台丽日扬善弘仁一片天', '呼朋赏月月行瀚海碧波间', '高朋满座倾心叙旧诉衷肠', '民生是本欣挥铁笔写风流', '和平共处五州友好共双赢', '满腔诚信长赢福利四时春', '诗联并进渝水巴山起异军', '人生不醉且看百年长恨歌', '穷途哭恸阮籍猖狂独咏怀', '杏林栽福地仁心妙术起沉疴', '台阶通化境佛寺巍巍气韵深', '一泓碧水闪闪翻波四季清', '有彩有华偶得佳联少雅人', '万锋笔健联台宿将舞龙文', '任丝织絍如果连编可纺纱', '慕拜袁黄奇门遁甲演五方', '落单鸿雁不回首疼坏西风', '水接桃源千载衣冠特堪尊', '八方铺锦绣紫燕娇啼羡物华', '塘边揽胜喜看绿水跃红鱼', '春溪赴梦入径带来山外情', '新企新社发展商机面貌新', '不怀医祖表里阴阳怎得通', '净化空间定让蓝天展笑容', '挥洒一身才气令岁月流芳', '寻梅雪岭无畏寒侵自有香', '琼花瑶叶雨浥芙蕖冉冉香', '皖吟风徽歌韵老村美景若诗', '家书有泪一行归雁向佳人', '和风二月燕剪裁红天地春', '读书堪备对好邀莲炬听更声', '羚羊跪乳孝亲报国展鸿遒', '中华娇子红塔山云烟贵烟', '字成一体大戟长枪跌宕书', '人言虽可信但防渭水混泾江', '年年七夕望月观星念恋人', '不带山不带水归途只带仙风', '勤习十载几案当知学子心', '神州筑梦四方创业业峥嵘', '弘扬传统开创未来再立功', '彩虹飞赤县道通八极路生金', '心存群众为民永作护旗人', '众生平等人我都从低处来', '江边楼上商女欢讴玉树歌', '湖面平和清心恬静意犹闲', '灭除瘟疫倾力堵封病染源', '楚山飞楚曲八方唱就楚风淳', '自己装车自负东京愧北京', '玉轮升碧海清辉广照出天然', '壮怀逸兴盛世鸿儒聚鹭园', '终日惟杜门蔬食经卷绳床', '我惭玉润时逢二月吊南州', '只身游燕赵淡泊无定水云舒', '执法文明素质高哪有霸王', '寒山一梦入耳钟声未必真', '如无真意休来假泪再丢人', '且移玉趾街坊人家结福缘', '云天碧水横练陈江七彩颜', '嫩竹舒新绿倚遍春风翠袖寒', '烘云托月水岸迢迢别样途', '司冬黑帝五湖四海欲凝冰', '皈依万象柳叶馥香传妙音', '心想即成创业顺利步步高', '心朝北斗祖国万岁路铺金', '新年缔良缘月圆人寿谱新歌', '平台屹屹出水蛟腾碧浪中', '四十年苦戍曾教瀚海变桑田', '山灵云逸泉流一脉抚瑶琴', '风亦软云亦淡独怜一地月华', '大呼小叫行住衣食快断流', '为环球献瑞沧桑洗礼万年冰', '篇篇墨语字字无非寂寞吟', '门生情切切敢捐大义铸心碑', '河声逐梦虽经九曲一条心', '站多和韵脚步踏欢快节拍', '三村海阔起碇悬帆赶早潮', '帆樯蔽日风送筝声多在船', '春光照大地九州共绘小康图', '心游翰海叹这般风月似醉似痴', '三农仁政乐浪九州动地来', '福临百姓家和业盛梦添香', '湖湘原道感悟人生万世雄', '亭自皇朝建青松擎月可知情', '意兴飞扬行舟更借一帆风', '莺鹂鸣柳恰有南风雁早乘', '风和牵细浪托盘荷畔捧玉珠', '挥扬旗帜复兴梦执政为民', '欢迎学者此道终须启后人', '千军同忾岂容鬼怪再猖狂', '一字桥头布谷偶听四五声', '一刊誉满誉骋楹联艺术家', '城苑真娇育德千秋桃李馨', '蟾光初照银桨徐摇万点星', '汇九霄圣脉犀江溢彩梦园芳', '丁香迎夏四面八<E99DA2><E585AB>
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: True\n",
|
|||
|
"liczba proponowanych wersów: 135\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.007407407407407408\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 一城增富丽壮气宇千秋启数篇锦绣词章麟阁喜添文苑笔\n",
|
|||
|
"poprawny wers drugi:\t 百福祝祥和夸峥嵘万象惊满目辉煌金碧花都沉醉岭南香\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['听竹赏梅以咏赊几分诗韵养三分淡泊丹青常寄竹梅清', '柳火煎茶柳翠鸣鹂柳营试马柳条赠别翠影千条咏赞多', '重描千卷蓝图更要靠山惜山靠水惜水当长思百代儿孙', '哭逝者青春忽碎痛矣十分十五六七韶华定格永恒', '填几行翠菊阅几阙丹霞诵几章朱岭吟成红谷春天', '事如云不甚详惟风骨惟馒头彼事情兮一时所啖尝耶', '天上人间于斯占尽更有丹摇翠涌烟霞遍染江山', '亲朋云集两三千看八方美景斑斓椿萱焕彩南岳峰巅竹生华', '皖山隔梦远且自横万里孤舟娥江暂泊更张不敢是乡音', '叹家无所业丧无所殡问为官世上几人廉洁似先生', '忠武祠堂希文谥法正气满寰中下则河岳上则日星', '凭栏问根祖何处是秦关汉阙兴衰以证看斯楼踞坐中州', '戎服读春秋亦英雄亦儒雅试认九霄正气常随奎壁焕光芒', '洵隽阳巨指主余社盟主全校事临危寄诗序开函如读岘山碑', '想必浮生犹有味君莫笑伍员卖唱秦琼卖马杨志卖刀', '禅堂肃穆回旋磬韵梵音弘扬佛法真经雄震乾坤', '盼你归来托雁捎两行书信菊花深处有人立尽斜阳', '邀客聆听胜地谐声诗中境界喜风传彩信鸟唱金歌', '韦驮传法后喜白龙拓境褐石遗痕紫溪流乳宝殿生辉', '右边杉立左视木偶似这样不肖弃材樵夫何妨劈面三刀', '大手笔宏开胜境植出葱茏巴蜀丰草长林两岸诗', '不择沙滩肥沃土可拒风侵何愁雨袭高怀落落谢春晖', '英魂不死倘狼烟再起东南鼓鼙掀海浪今朝卫土效文襄']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: False\n",
|
|||
|
"liczba proponowanych wersów: 23\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.0\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 山地畅通用安全铺路\n",
|
|||
|
"poprawny wers drugi:\t 油田崛起为生产护航\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['情牵大地春满人间', '珠圆玉润入口皆甜', '德行梦笔开盛世新篇', '柔水月光披野地天穹', '去期颐仅廿载后福无疆', '春来之明灯爆竹贴红联', '诗书启后勤俭传家', '雨打荷叶叶成泪滴滴成珠', '满身花影倩人扶', '任丝织絍如果连编可纺纱', '猴腾广宇绽琼花', '花明柳媚湖上长春', '调一湖春色染绿江淮', '河清海晏让人迷', '一联争首榜元眼花胪', '鸿才立世展鸿图', '莲花亲水意崇廉', '湖山叠韵入我诗囊', '姜维奇术揣摩诸葛计谋', '笔点涟漪见水平', '瑞通阆苑琼楼兴百轩', '笔如磨剑要藏锋', '砖雕雕壁画砖马腾空', '国持德政著宏篇', '劈开天地定人伦', '神驹腾跃吉祥年', '赋浓夏盛寓秋实', '东床配西席不是东西', '中华共颂贤臣', '桨声翻学海海载苦舟', '官居宰相望王侯', '共赏芦溪水高下相倾', '元兴世盛展宏图', '烘云托月水岸迢迢别样途', '五光十色文字之华', '这边环境安宁', '柳垂水面翠溶南北风', '小金龙瑞雪兆丰年', '诗礼之教家人利贞', '攀龙附凤欲攀彩凤缘', '梅乡舟里唱瘦月两回', '地连南北日星恒久晖', '水城画卷展宏图', '爱无边意无限父老堪亲', '度日如年席卷八荒', '热情周到锦上添花', '廉风动地畅山河', '辩雕春囿德莹秋天', '邀月吟诗诗藏梦海碧波里', '微言明义苦谏纠偏', '尘凡皆妄昧贪嗔痴愚', '满园桃李何言北大荒', '龙跃凤鸣南渡江边雅士多', '平野百里高山九重', '当知沦落也从容', '鸣钟食鼎甘田土之出', '平沙戏马雨声干', '手携一集质于通人', '碧柳迎春山河送惠风', '廉风集聚兴廉务必清廉', '弥陀含笑放光辉', '绿荫浓清山美有凤来仪', '金猴贺新岁岁岁平安', '律己循规永葆廉风', '飞腾雅典腾飞环球', '风回柳苑韵邀风', '美水美山美景美未来', '花样年华联若洒可钦']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: False\n",
|
|||
|
"liczba proponowanych wersów: 68\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.0\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 随花归故里\n",
|
|||
|
"poprawny wers drugi:\t 伴梦眠老屋\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['人懒几生尘', '壮志献江山', '清心长保真', '浪漫怕新闻', '草木已含英', '弦断梦难圆', '作业构三章', '道德五千', '四海奋人心', '功犹可迁', '兰气盈庭', '珍簟展方床', '贤媳举扇陪', '美誉眼前风', '仰百年师', '浓厚简约虚', '莫愁女儿红', '此味几人同', '头彩出中原', '撒豆成兵', '绿野寄仙踪', '倾城倾国', '智者忍违缘', '梅韵贺新年', '绿茵陈', '一街太平歌', '诗带好风吟', '赤水得玄珠', '月分老梅香', '高第煦春风', '禹甸沐春风', '抑抑威仪', '游子自存心', '心悟得真', '道理甚分明', '移山志不忘', '醉后赋离骚', '胡蝶飞南园', '缘去梦依然', '风笔绘春秋', '三江福寿图', '妙理贵躬行', '高处看浮云', '足写乾坤', '牖含遍岭春', '冬夜暖开心', '众号神君', '龙女牧羊', '随地皆春', '秋雨秋风', '党赐深恩', '池浅韵牵波', '宛在岱中行', '天禄谈经', '王府池子深', '寺与山争鲜', '贪后买官', '眉月静横窗', '酒醉好题诗', '冗鱼', '醉酒吐真情', '年年有盼头', '碧浪皱红霞', '淡雅雪边梅', '养性延年', '菡萏静生香', '伴梦眠老屋', '禅味涤心胸', '民以食为天', '初日临春虚', '恨别鸟惊心', '鹤立水中央', '福禄寿喜', '苔湿地刁皮', '鸟语落花山', '百岭见千娇', '才子佳人', '小曲品三春', '人我法皆空', '树葱茏', '艺高大胆人', '花荣上海人', '天地月常圆', '导义延仁', '时泰喜黎民', '而今当宝存', '春入鸟能言', '偏遇有情人', '大宴高轩', '白日奈我何', '无肉也能行', '上寿可期', '夜寂鸟啼空', '塞外朔风寒', '学子话春浓', '巧拙尚相悬', '两手作生涯', '方士信求仙', '中庭松桂姿', '品德讲道德', '出入有声名', '莺歌鹧鸪天']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: True\n",
|
|||
|
"liczba proponowanych wersów: 102\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.00980392156862745\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 游福地赏风光苏岭郴江铺锦绣\n",
|
|||
|
"poprawny wers drugi:\t 款嘉宾谈经贸南湘林邑创辉煌\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['身名归泡幻抟风羽翼伤心岂独帝京篇', '陈家颜割落耳朵颜面才是东家', '悠悠矣少小离家潇洒人生梦已开', '坐中都是词杰酒半酣时眼更狂', '一时三刻下大雨免道士多啄狗头', '十年非忘本学子该当底气足', '台阶通化境佛寺巍巍气韵深', '香由心生念嫦娥娥寝不离桂花香', '六万数余银充库奉公素抱藿葵心', '款嘉宾谈经贸南湘林邑创辉煌', '八方铺锦绣紫燕娇啼羡物华', '但见波摇影荡不知何处是仙源', '客属同源客家共脉何处不生故土情', '离别时章柳折残山花静待来春', '强国兴邦关注三农百业展新猷', '赏山川斯方似画云中阆苑璨尧天', '梅花千万点报得人间锦绣春', '鲲鹏翔瀚宇激越高昂自在身', '啜甘须忆苦纵登高位犹纳清风', '南为火北为水自古水火怎相容', '东南窥胜境五峰接壤让他丰骨独高骞', '嫩竹舒新绿倚遍春风翠袖寒', '九州畅百川深深悟重和天下论治平', '河涌万顷碧浪太阳圣曲震神州', '江城子思渔父遥寄巫山一片云', '开怀八大味五味滋身三味养心', '浮萍漂泊水中花花中水水中花中', '更漏子蝶恋花千滴满见泪沙流', '桃源美景诗心自醉但邀竹下七贤', '心游翰海叹这般风月似醉似痴', '电城煤城林城一城发展舞龙头', '亭自皇朝建青松擎月可知情', '传家无别业惟薄田数亩旧书五车', '梅影横窗瘦南枝微弄雪精神', '今成瑰宝家藏一件历世定瓷倍足珍', '卢敖得道浮丘放鹤福地神仙去复来', '胸怀税务戮力耕耘收税献丹心', '德宏章贡修文悦礼敦古铄今', '琉球皇帝诏列屿飘零时时在故国神游', '傅说堪圆一梦得以兴邦果圣人', '动车牵北南绿城煤城双轨接龙', '荷叶一池满铺开澄碧坦荡人心', '盼美丽中国收入倍增成就小康']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: True\n",
|
|||
|
"liczba proponowanych wersów: 43\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.023255813953488372\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 富春垂钓\n",
|
|||
|
"poprawny wers drugi:\t 天禄谈经\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['人懒几生尘', '壮志献江山', '清心长保真', '浪漫怕新闻', '草木已含英', '弦断梦难圆', '作业构三章', '兰馨溢神州', '道德五千', '四海奋人心', '功犹可迁', '兰气盈庭', '珍簟展方床', '贤媳举扇陪', '美誉眼前风', '仰百年师', '莫愁女儿红', '此味几人同', '头彩出中原', '撒豆成兵', '绿野寄仙踪', '慢煲绿豆汤', '倾城倾国', '智者忍违缘', '德及乡里', '梅韵贺新年', '绿茵陈', '一街太平歌', '桃花自美容', '诗带好风吟', '大功扫叛臣', '赤水得玄珠', '月分老梅香', '高第煦春风', '禹甸沐春风', '抑抑威仪', '一樽欢暂同', '游子自存心', '雪厚松袅云', '道理甚分明', '庙略久论兵', '重担重担人', '碧柳锁长亭', '醉后赋离骚', '胡蝶飞南园', '缘去梦依然', '风笔绘春秋', '妙理贵躬行', '足写乾坤', '气化三清', '牖含遍岭春', '冬夜暖开心', '豆灯照墨新', '众号神君', '龙女牧羊', '随地皆春', '秋雨秋风', '党赐深恩', '池浅韵牵波', '宛在岱中行', '天禄谈经', '寺与山争鲜', '夕观沧海云', '贪后买官', '眉月静横窗', '酒醉好题诗', '梅迎跃进春', '思量枕席功夫', '家庭祥和', '醉酒吐真情', '年年有盼头', '碧浪皱红霞', '淡雅雪边梅', '养性延年', '民心向党红', '菡萏静生香', '伴梦眠老屋', '禅味涤心胸', '民以食为天', '初日临春虚', '恨别鸟惊心', '朝槿散幽香', '鹤立水中央', '福禄寿喜', '陕州人杰灵', '苔湿地刁皮', '百岭见千娇', '才子佳人', '小曲品三春', '人我法皆空', '艺高大胆人', '花荣上海人', '天地月常圆', '一鳞', '导义延仁', '人品甘没闻', '时泰喜黎民', '而今当宝存', '春入鸟能言', '偏遇有情人', '大宴高轩', '无肉也能行', '上寿可期', '夜寂鸟啼空', '塞外朔风寒', '学子话春浓', '巧拙尚相悬', '两手作生涯', '方士信求仙', '品德讲道德', '秋波我梦吟', '出入有声名']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: True\n",
|
|||
|
"liczba proponowanych wersów: 112\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.008928571428571428\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 闻思修并重\n",
|
|||
|
"poprawny wers drugi:\t 人我法皆空\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['人懒几生尘', '壮志献江山', '清心长保真', '浪漫怕新闻', '草木已含英', '松摇古谷风', '弦断梦难圆', '作业构三章', '兰馨溢神州', '四海奋人心', '珍簟展方床', '贤媳举扇陪', '美誉眼前风', '冰封万水寒', '浓厚简约虚', '莫愁女儿红', '春归柳色红', '此味几人同', '头彩出中原', '绿野寄仙踪', '玉律始调阳', '难教白日闲', '智者忍违缘', '真风再发扬', '梅韵贺新年', '转瞬万山遥', '一街太平歌', '桃花自美容', '子亦来见我乎', '诗带好风吟', '长短尽随风', '大功扫叛臣', '赤水得玄珠', '诗兴不无神', '月分老梅香', '高第煦春风', '禹甸沐春风', '徒临洗药泉', '一樽欢暂同', '游子自存心', '雪厚松袅云', '案头月一樽', '道理甚分明', '木栽门内闲', '庙略久论兵', '重担重担人', '移山志不忘', '碧柳锁长亭', '醉后赋离骚', '胡蝶飞南园', '世态笑炎凉', '缘去梦依然', '风笔绘春秋', '三江福寿图', '妙理贵躬行', '脉脉万重心', '高处看浮云', '两乡明月心', '高悬不畏风', '牖含遍岭春', '冬夜暖开心', '少年是网虫', '豆灯照墨新', '水凉难泡茶', '宛在岱中行', '蝉噪涧才幽', '王府池子深', '寺与山争鲜', '夕观沧海云', '眉月静横窗', '酒醉好题诗', '梅迎跃进春', '箫声向远天', '莫向外头看', '思量枕席功夫', '松风如在弦', '这边环境安宁', '醉酒吐真情', '年年有盼头', '碧浪皱红霞', '山深虎迹踪', '民心向党红', '菡萏静生香', '伴梦眠老屋', '禅味涤心胸', '民以食为天', '初日临春虚', '三春经纬织民图', '恨别鸟惊心', '朝槿散幽香', '惩凶儆效尤', '鹤立水中央', '福禄寿喜', '梅花落我肩', '陕州人杰灵', '鸟语落花山', '搴舟破晓风', '百岭见千娇', '衣间不带尘', '小曲品三春', '人我法皆空', '艺高大胆人', '花荣上海人', '天地月常圆', '红雨浸黄云', '敞襟天地宽', '人品甘没闻', '开光佛自由', '时泰喜黎民', '月轮碾古今', '而今当宝存', '春入鸟能言', '偏遇有情人', '落草潜伏十字坡', '心静自然凉', '山转路无穷', '白日奈我何', '春心蝶最知', '千花夹寺门', '无肉也能行', '夜寂鸟啼空', '江涌古今潮', '尝鲜食鱼羊', '塞外朔风寒', '学子话春浓', '巧拙尚相悬', '两手作生涯', '方士信求仙', '中庭松桂姿', '云外一声钟', '秋波我梦吟', '出入有声名', '莺歌鹧鸪天']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: True\n",
|
|||
|
"liczba proponowanych wersów: 133\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.007518796992481203\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 飞桥驾鹊天津阔\n",
|
|||
|
"poprawny wers drugi:\t 平沙戏马雨声干\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['人如无欲意何求', '举帜遵章共展才', '岩上青藤攀壁升', '济助家乡晃美名', '草内多藏五步蛇', '病入膏肓有治疗', '毅力凝成跨海桥', '后乐先忧范弟昆', '疑是瑶台月下逢', '锦绣春归百姓家', '鼓瑟还从曲里来', '雀跃鱼翔谐乐多', '泉瀑飞长水练生', '雾里青山画屏开', '暮忆三秋雁字长', '烟柳风丝拂岸斜', '竹林满山景隽幽', '悦己悦人悦世间', '阔水滔滔有酒仙', '燕子三双戏柳烟', '豪赌毁他上进心', '野渡闲游一叶舟', '含露芙蓉醉海棠', '日月同辉光景嫣', '可恨蛮牛不识琴', '酒醉不如伴月眠', '草木逢春年年生', '画就雾云笔墨香', '悔被浮名牵累多', '坑我此生此袋烟', '笔盖古今三千年', '克俭尚勤播誉名', '规矩者应晓方圆', '猴腾广宇绽琼花', '言少言多尽美谈', '花狎春云露搅和', '大漠孤烟古道长', '新颖文章秋水清', '国泰民安幸福多', '南燕离巢北国春', '沸沸扬扬世界杯', '浅草雷门愧下关', '慧智还从实践来', '融月新醅慢慢尝', '绿岛清风拂袖来', '电力惠民百业兴', '千树争高有健才', '学问无穷博古今', '富裕安康福万家', '国运弥盛史弥远', '时急方须济世才', '公正不阿辩是非', '祭酒干杯国子光', '国展宏图烈士欣', '碧野连天满目春', '糊口养家望父滩', '大河滚滚尽淘沙', '淡淡菊香盈袖中', '笔点涟漪见水平', '弱不禁风是书生', '动动脑筋动静无', '玉笛金弦赞独生', '怪句洞开谜底联', '不叫俗尘污本真', '喜报频传战士家', '竹韵梅香总可人', '一指清凉尽染秋', '小小儿郎立路中', '意气风发马晓春', '无田有业不为贫', '涛落沙新畔易留', '德雨润开廉洁花', '月老三分秋水寒', '听竹尤增几许清', '大丈夫能屈能伸', '弹毕雅曲听和声', '人爱人钦人喜欢', '洞口经春长薜萝', '莫指云山认故乡', '韧节有意杜虚名', '洗出芙蓉九点青', '草木蔫枯晒绿洲', '落日也将暮色描', '象郡云烟锁桂梧', '潋滟江波扑簌风', '人且清心同步行', '名享三奇显祖公', '明月来时渚落霜', '阳朔沿水显花荣', '绿水卧听新月明', '北海波清映日黄', '映月二泉人世情', '世间最难得弟兄', '枝上空吹故国风', '笑问书生君是谁', '孔圣有才死后尊', '翠柳清风伴杏娇', '新庆交封暨缅封', '往事依然笔底新', '风过泸州带酒香', '相思一点老了谁', '风雨人生鉴知音', '小觑浮名对酒歌', '早出晚归皆自然', '翠袖拂空一抹烟', '却为心肝伤脑筋', '达业欣成万户楼', '点水蜻蜓赏绿来', '重义轻财德道深', '道士身怀童子功', '步步登高上岳阳', '热血沸腾意若何', '对苑繁华万蕾新', '宝地佛临济世人', '万里山光收画屏', '一捧廉泉岛外春', '防不胜防贼近身', '知耻明荣胸臆宽', '每觉邻山云最多', '虎步春光翼又添', '笔墨书空自忘机', '绿净春深好染衣', '常想旁通不对头', '一意孤行不回头', '烛影摇红步步娇', '路远始于跬步间', '江水源源发电来', '纵览清江高士怀', '关羽无停觅长兄', '落木落红落寂生', '小可参禅入几分', '九世同居号义门', '空海星辰宇宙流', '兄弟同吟夜雨陪', '贞慧何辞驻翠颜', '竹韵真箫彻夜吹', '落日栖霞赏故园', '正在柳洲接柳风', '文庙弦音奏凯频', '却诩心田少欠情', '山影盘龙月钓珠', '崇廉尚德岛尚书', '大势所趋水如蓝', '何处箫声断客肠', '皓月两轮水面逢', '良夜清风月满湖', '仙境田园隐棹声', '白发无情忽上头', '掷笔从戎壮士名', '夫再礼让妻再争', '三爱首推书友茶', '绝代佳人不入俗', '黄叶飘零比较烦', '剌史同游忆月明', '玉液溶溶滴露来', '骨头坚硬好八连', '黄金灿灿冷如冰', '四海龙兴艺术潮', '杨絮舞出风感觉', '玉鼎沉香影寂寥', '德作福田三世修', '小阁亦存明月身', '十<>
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: False\n",
|
|||
|
"liczba proponowanych wersów: 266\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.0\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"for indeks_wersu_pierwszego in wybrane_dane_testowe:\n",
|
|||
|
" wers_pierwszy = testowe[\"in\"][indeks_wersu_pierwszego]\n",
|
|||
|
" print(\"wers pierwszy:\\t\\t\", wers_pierwszy)\n",
|
|||
|
" poprawny_wers_drugi = testowe[\"out\"][indeks_wersu_pierwszego]\n",
|
|||
|
" print(\"poprawny wers drugi:\\t\", poprawny_wers_drugi)\n",
|
|||
|
" print()\n",
|
|||
|
"\n",
|
|||
|
" reprezentacja_wersu_pierwszego = x_test[indeks_wersu_pierwszego]\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego = []\n",
|
|||
|
" for indeks_wersu_drugiego in range(len(y_test)):\n",
|
|||
|
" reprezentacja_wersu_drugiego = y_test[indeks_wersu_drugiego]\n",
|
|||
|
" wejscie_do_MLP = torch.cat((reprezentacja_wersu_pierwszego, reprezentacja_wersu_drugiego))\n",
|
|||
|
" if klasyfikator.predict([wejscie_do_MLP])[0] == 1:\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego.append(indeks_wersu_drugiego)\n",
|
|||
|
"\n",
|
|||
|
" proponowane_wersy = [testowe[\"out\"][i] for i in mozliwe_indeksy_wersu_drugiego]\n",
|
|||
|
" print(\"proponowane drugie wersy:\", proponowane_wersy)\n",
|
|||
|
" print(\"czy poprawny wers jest pośród proponowanych wersów?:\", poprawny_wers_drugi in proponowane_wersy)\n",
|
|||
|
" print(\"liczba proponowanych wersów:\", len(proponowane_wersy))\n",
|
|||
|
" print()\n",
|
|||
|
"\n",
|
|||
|
" print(\"wynik przyjętej metryki:\", jagosz_score(poprawny_wers_drugi, proponowane_wersy))\n",
|
|||
|
" print()\n",
|
|||
|
" print(\"-\"*50)\n",
|
|||
|
" print()"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "Cw1nw0D6zjRm"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"## MLPRegressor\n",
|
|||
|
"### Proponuje wszystkie wersy, dla których ocena modelu jest większa niż 0,9."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 91,
|
|||
|
"metadata": {
|
|||
|
"id": "nxQUlqdAzjRm",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "d5b6bddf-4cbb-4251-b5bb-11cb7b936b9a"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"wers pierwszy:\t\t 花好月圆涵画意\n",
|
|||
|
"poprawny wers drugi:\t 年丰人寿沁诗声\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['尘烟已远隐青山', '而今华夏振雄风', '他乡月好俺思亲', '火山光灿地遭殃', '三杯白酒乐成仙', '峰平径长难藏景', '海深寻秘展雄才', '青山醉向一樽横', '好同蝉窟映三潭', '铮铮梅蕾半含春', '莺燕对舞艳阳天', '一心二用两头空', '闲庭信步哼欢歌', '偶观雨燕栖寒檐', '居身常抱玉壶清', '清风两袖带回家', '三阳开泰颂廉明', '小楼吹砌玉生寒', '锦葵昂面为迎光', '看三国志欲何为', '闹中取静看擂台', '与君同作太平人', '青灯久作故人看', '猴腾广宇绽琼花', '好留明月九千秋', '风临荷盏窃清香', '古人异代不同时', '弄潮帆影港城新', '堤前柳浪露春光', '春江柳线乱弹琴', '梅香葱岭缀长虹', '仙人指路点迷津', '梧桐叶上得秋声', '无田有业不为贫', '一溪柳绿到谁家', '风临柳榭露春心', '俨然天竺古先生', '武夷阳羡品俱馨', '远山终日送余霞', '笛声浅扣暗推窗', '一川杨柳笼和风', '半空摇晃寻常仁', '追求亮丽美人图', '雪融春到春融雪', '常将劲节负秋霜', '善男信女拜观音', '劈开天地定人伦', '举杯邀月到凡尘', '伤心羁旅断愁肠', '夜灯勤礼塔中仙', '赋浓夏盛寓秋实', '牢盆给费利官民', '飞鸿远浦一时惊', '蛇听燕语颂春光', '清泉有趣自通融', '游春岂料梦成真', '年丰人寿沁诗声', '霜飞两鬓孔明灯', '不言第一海胸襟', '乌啼古树惹乡愁', '难经何必借炎黄', '子孙常读未烧书', '情凝大地重如山', '长将远景引天边', '有悲寒户落新愁', '满腔忧愤铸诗魂', '万般殷切候佳音', '行吟战马啸征尘', '何堪永夜漏更寒', '荷描夏画日钤章', '漫天风语绕苍穹', '花贴幼子悦童心', '红旗漫卷息狼烟', '四行热泪洒苍颜', '人间重义树新风', '一言九鼎定心神', '春风念字到青青', '观光农业载风情', '钻杆穿地唱欢歌', '再将粉黛沁于宣', '秉公执法树廉风', '当惊阁老好风光', '梅花傲雪迓长春', '出门去白面书生', '江郎梦里得犹神', '畅谈国事一腔情', '黄金灿灿冷如冰', '春深似海梦无痕', '文中已现老成心', '迎春老树发新芽', '无休往日浩如烟', '相思不减病扶墙', '挖坑华夏葬儒顽', '江心美景湛空明', '吸烟无益肺摧残', '风吹枫落枫随风', '蔼峰亦寄诗仙情', '江山忧患老英雄', '收篇难阻浪涛狂', '黄粱入梦悟尘心', '火牛曾胜敌千军', '杯中寂寞不曾空', '风流绝壁写春秋', '风流人物看今朝', '廉风动地畅山河', '帆连水色接天涯', '酣摊夏苑恋风情', '人生一笑尽良朋', '灯残襟冷感情无', '满园桃李尽争春', '碧峰犹冷寺前春', '千竿节气叠浪花', '人威毕竟胜天威', '千枝红杏闹春光', '仙风道骨验方肠', '草逢蓬室至家中', '廉政为民常山情', '三园猗顿晋商宗', '春歌大地爱国情', '隔村香送稻花肥', '蛇头就是做中人', '浓妆淡抹总相宜', '长江十堰显神功', '汉宫春风暮烟中', '春逢喜气盛迎门', '丹楹喜庆福临门', '亭空不碍鸟穿行', '慵眠古渡淡千愁', '收来花信燕声中', '文章有道拟施行', '闻名色变探花郎', '弥陀含笑放光辉', '闲生百态网中人', '转身应把泪珠弹', '千秋剑气护忠魂', '五申三令不成规', '幅巾嘉论有清风', '三生有幸遇知音', '峰丘暗许百年情', '一窗竹影又经风', '无情霜剑毁金枝', '不甘卖命换虚名', '风回柳苑韵邀风', '烟波浩渺任龙舒', '昙花怎晓夜幽长', '名城泉水润京腔', '搏风远去水云间', '笑迎世纪浴春光', '两行白鹭上青天', '笑谈成败慎出兵']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: True\n",
|
|||
|
"liczba proponowanych wersów: 150\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.006666666666666667\n",
|
|||
|
"wyjaśnienie - największe wartości przewidywań\n",
|
|||
|
" indeks wartosc\n",
|
|||
|
"1077 1077 1.750754\n",
|
|||
|
"322 322 1.719922\n",
|
|||
|
"1334 1334 1.606502\n",
|
|||
|
"1386 1386 1.572082\n",
|
|||
|
"80 80 1.496093\n",
|
|||
|
"... ... ...\n",
|
|||
|
"1028 1028 0.909767\n",
|
|||
|
"81 81 0.908926\n",
|
|||
|
"151 151 0.907361\n",
|
|||
|
"949 949 0.904659\n",
|
|||
|
"129 129 0.897735\n",
|
|||
|
"\n",
|
|||
|
"[151 rows x 2 columns]\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 人家不必论贫富\n",
|
|||
|
"poprawny wers drugi:\t 唯有读书声最佳\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['把酒依然大丈夫', '雀跃鱼翔谐乐多', '千般柳絮游子心', '映日桑榆重晚晴', '悦己悦人悦世间', '豪赌毁他上进心', '木讷的人难启迪', '可恨蛮牛不识琴', '笔盖古今三千年', '克俭尚勤播誉名', '玉律始调阳', '真风再发扬', '地阔难及贪欲长', '璋玉无瑕耀祥光', '沸沸扬扬世界杯', '梦里飞花静闻香', '栽培桃李成林', '学问无穷博古今', '富裕安康福万家', '时急方须济世才', '公正不阿辩是非', '糊口养家望父滩', '笔点涟漪见水平', '怪句洞开谜底联', '不叫俗尘污本真', '喜报频传战士家', '涛落沙新畔易留', '胜算亦防失误时', '听竹尤增几许清', '大丈夫能屈能伸', '弹毕雅曲听和声', '爱慕虚荣唱一出', '春梦几枝与醉痴', '落日也将暮色描', '名享三奇显祖公', '唯有读书声最佳', '孔圣有才死后尊', '牢盆给费利官民', '风过泸州带酒香', '三令五申还有贪', '热血沸腾意若何', '知耻明荣胸臆宽', '常想旁通不对头', '优良业绩绩可观', '关羽无停觅长兄', '贞慧何辞驻翠颜', '马舞龙韵续华章', '大势所趋水如蓝', '白发无情忽上头', '掷笔从戎壮士名', '剌史同游忆月明', '腹有奸谋即兽心', '室壁裂时蟢网缝', '十里桃花相见欢', '百年盟约好时光', '静夜遐思枕月眠', '竹下新笋一色鲜', '俯首甘为孺子牛', '水稻风多不待秋', '圣子甘心为罪囚', '出口频频亿万吨', '谁到篱前问姓名', '部长铁男不定还', '慷慨悲歌魏晋风', '载笔须来阙下游', '无主孤魂百姓怜', '兰桂齐芳福乐门', '饮酒月前独自愁', '何防凿壁偷', '刻炬成诗韵可观', '处世何须带伪装', '古木生芽不是春', '老虎苍蝇一起除', '落草潜伏十字坡', '天乐鸣时简子游', '是大英雄自虚怀', '竹色四时也不移', '美色必将随后衰', '雅意如茶自在闲', '落笔再歌吉祥年', '水墨胡涂浪漫稀', '紫燕翻飞柳泛青', '百千万亿归于零', '河朔膏腴古督亢', '花好焉无惬意时', '何必杀鸡笑野猴', '南粤万家景色新']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: True\n",
|
|||
|
"liczba proponowanych wersów: 87\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.011494252873563218\n",
|
|||
|
"wyjaśnienie - największe wartości przewidywań\n",
|
|||
|
" indeks wartosc\n",
|
|||
|
"1248 1248 1.538035\n",
|
|||
|
"132 132 1.512274\n",
|
|||
|
"33 33 1.486148\n",
|
|||
|
"1464 1464 1.430674\n",
|
|||
|
"259 259 1.351068\n",
|
|||
|
"... ... ...\n",
|
|||
|
"1270 1270 0.904546\n",
|
|||
|
"799 799 0.903461\n",
|
|||
|
"401 401 0.903190\n",
|
|||
|
"1080 1080 0.903127\n",
|
|||
|
"648 648 0.899576\n",
|
|||
|
"\n",
|
|||
|
"[88 rows x 2 columns]\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 岳麓求知精通科技千秋重\n",
|
|||
|
"poprawny wers drugi:\t 湖湘原道感悟人生万世雄\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['瑶花雅洁馨香海国铸精神', '风如云清清云清风枕边风', '双龙闹海茫茫大海我争雄', '贤也圣也慢步详窥玉洞云', '情歌依旧仍随秋水染夕阳', '休闲有乐掌中点击地球村', '瑶台丽日扬善弘仁一片天', '民生是本欣挥铁笔写风流', '茗标熠熠全身银泽溢新馨', '一泓碧水闪闪翻波四季清', '落单鸿雁不回首疼坏西风', '塘边揽胜喜看绿水跃红鱼', '春溪赴梦入径带来山外情', '净化空间定让蓝天展笑容', '斑斑旧迹义垂万古一山魂', '寻梅雪岭无畏寒侵自有香', '琼花瑶叶雨浥芙蕖冉冉香', '家书有泪一行归雁向佳人', '和风二月燕剪裁红天地春', '说地谈天妙语千词趣味生', '新梨屯垦耕联苑国粹拓疆', '年年七夕望月观星念恋人', '弘扬传统开创未来再立功', '心存群众为民永作护旗人', '湖面平和清心恬静意犹闲', '旌旗飞舞千桡激起粤精神', '壮怀逸兴盛世鸿儒聚鹭园', '碧血丹心一缕幽香溢九州', '寒山一梦入耳钟声未必真', '地铁迎春西咸大道正龙吟', '且移玉趾街坊人家结福缘', '云天碧水横练陈江七彩颜', '皈依万象柳叶馥香传妙音', '平台屹屹出水蛟腾碧浪中', '亮相走红依卖弄不类不伦', '篇篇墨语字字无非寂寞吟', '门生情切切敢捐大义铸心碑', '河声逐梦虽经九曲一条心', '庭前漫步闲听寂寂落花声', '正本清源革故鼎新事业昌', '三农仁政乐浪九州动地来', '曙开平野露沾萱圃草悬光', '地比巴黎精描山水化诗章', '莺鹂鸣柳恰有南风雁早乘', '欢迎学者此道终须启后人', '一刊誉满誉骋楹联艺术家', '蟾光初照银桨徐摇万点星', '丁香迎夏四面八方雨亦催', '春心激动重温旧梦老地方', '慈容宛在莲邦从此乐逍遥', '大道生辉养性修身万世春', '立于天地处事当留一点真', '壮大联坛一片云霞灿锦城', '红光一色欢天喜地满堂春', '民情在抱两袖清风促和谐', '槐花怒放五月槐花醉九龙', '锤镰记取红色党旗血染成', '千帆竞渡追梦宏开万里程', '月月风风叫你顿首献感情', '啼莺恰恰花盈秀野闹新春', '辉煌禹甸水漾芙蕖万象新']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: False\n",
|
|||
|
"liczba proponowanych wersów: 61\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.0\n",
|
|||
|
"wyjaśnienie - największe wartości przewidywań\n",
|
|||
|
" indeks wartosc\n",
|
|||
|
"63 63 1.430051\n",
|
|||
|
"328 328 1.232970\n",
|
|||
|
"285 285 1.195808\n",
|
|||
|
"812 812 1.183949\n",
|
|||
|
"1268 1268 1.162685\n",
|
|||
|
"... ... ...\n",
|
|||
|
"874 874 0.902322\n",
|
|||
|
"963 963 0.901936\n",
|
|||
|
"628 628 0.901397\n",
|
|||
|
"1267 1267 0.900050\n",
|
|||
|
"1306 1306 0.897187\n",
|
|||
|
"\n",
|
|||
|
"[62 rows x 2 columns]\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 一城增富丽壮气宇千秋启数篇锦绣词章麟阁喜添文苑笔\n",
|
|||
|
"poprawny wers drugi:\t 百福祝祥和夸峥嵘万象惊满目辉煌金碧花都沉醉岭南香\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['柳火煎茶柳翠鸣鹂柳营试马柳条赠别翠影千条咏赞多', '想必浮生犹有味君莫笑伍员卖唱秦琼卖马杨志卖刀', '邀客聆听胜地谐声诗中境界喜风传彩信鸟唱金歌', '心铭国耻胜利迎来铸辉煌功业耸立千秋纪念碑']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: False\n",
|
|||
|
"liczba proponowanych wersów: 4\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.0\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 山地畅通用安全铺路\n",
|
|||
|
"poprawny wers drugi:\t 油田崛起为生产护航\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['满身花影倩人扶', '调一湖春色染绿江淮', '梦里飞花静闻香', '瑞通阆苑琼楼兴百轩', '雪融春到春融雪', '桨声翻学海海载苦舟', '柳垂水面翠溶南北风', '梅乡舟里唱瘦月两回', '半坡翠竹耸蓝天', '满园桃李何言北大荒', '鸣钟食鼎甘田土之出', '金猴贺新岁岁岁平安', '花样年华联若洒可钦']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: False\n",
|
|||
|
"liczba proponowanych wersów: 13\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.0\n",
|
|||
|
"wyjaśnienie - największe wartości przewidywań\n",
|
|||
|
" indeks wartosc\n",
|
|||
|
"1123 1123 1.173290\n",
|
|||
|
"642 642 1.102260\n",
|
|||
|
"396 396 1.074446\n",
|
|||
|
"200 200 1.069938\n",
|
|||
|
"1409 1409 1.034693\n",
|
|||
|
"789 789 1.015779\n",
|
|||
|
"910 910 0.993425\n",
|
|||
|
"1341 1341 0.989088\n",
|
|||
|
"1018 1018 0.970223\n",
|
|||
|
"486 486 0.936643\n",
|
|||
|
"236 236 0.932841\n",
|
|||
|
"340 340 0.924875\n",
|
|||
|
"1181 1181 0.909370\n",
|
|||
|
"585 585 0.899589\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 随花归故里\n",
|
|||
|
"poprawny wers drugi:\t 伴梦眠老屋\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['此地是桃溪深处渊源一派溯龙泓', '浓厚简约虚', '此味几人同', '撒豆成兵', '德及乡里', '绿茵陈', '长短尽随风', '风定水无波', '醉后赋离骚', '气化三清', '众号神君', '党赐深恩', '池浅韵牵波', '宛在岱中行', '碧浪皱红霞', '初日临春虚', '才子佳人', '树葱茏', '出入有声名']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: False\n",
|
|||
|
"liczba proponowanych wersów: 19\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.0\n",
|
|||
|
"wyjaśnienie - największe wartości przewidywań\n",
|
|||
|
" indeks wartosc\n",
|
|||
|
"1104 1104 1.530875\n",
|
|||
|
"258 258 1.430391\n",
|
|||
|
"848 848 1.106179\n",
|
|||
|
"252 252 1.093159\n",
|
|||
|
"931 931 1.085685\n",
|
|||
|
"192 192 1.083769\n",
|
|||
|
"154 154 1.082355\n",
|
|||
|
"1445 1445 1.076470\n",
|
|||
|
"651 651 1.042010\n",
|
|||
|
"291 291 1.032749\n",
|
|||
|
"1081 1081 1.011256\n",
|
|||
|
"601 601 1.008619\n",
|
|||
|
"692 692 1.003235\n",
|
|||
|
"439 439 1.001311\n",
|
|||
|
"682 682 1.000212\n",
|
|||
|
"432 432 0.959261\n",
|
|||
|
"540 540 0.940793\n",
|
|||
|
"92 92 0.916658\n",
|
|||
|
"175 175 0.900608\n",
|
|||
|
"87 87 0.890902\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 游福地赏风光苏岭郴江铺锦绣\n",
|
|||
|
"poprawny wers drugi:\t 款嘉宾谈经贸南湘林邑创辉煌\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['悠悠矣少小离家潇洒人生梦已开', '此地是桃溪深处渊源一派溯龙泓', '诉久长诉离别相逢于白露欲来时', '其性烈其情柔其节亮留国史万年', '开怀八大味五味滋身三味养心', '浮萍漂泊水中花花中水水中花中', '涵汾水馨香太行厚重三晋同为一部书', '心游翰海叹这般风月似醉似痴', '传家无别业惟薄田数亩旧书五车', '红木映红心商德融器具造福万家']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: False\n",
|
|||
|
"liczba proponowanych wersów: 10\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.0\n",
|
|||
|
"wyjaśnienie - największe wartości przewidywań\n",
|
|||
|
" indeks wartosc\n",
|
|||
|
"92 92 1.294912\n",
|
|||
|
"962 962 1.177321\n",
|
|||
|
"1403 1403 1.120041\n",
|
|||
|
"888 888 1.088421\n",
|
|||
|
"1070 1070 1.044999\n",
|
|||
|
"868 868 0.995907\n",
|
|||
|
"78 78 0.967438\n",
|
|||
|
"885 885 0.909440\n",
|
|||
|
"522 522 0.902096\n",
|
|||
|
"941 941 0.901670\n",
|
|||
|
"85 85 0.876454\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 富春垂钓\n",
|
|||
|
"poprawny wers drugi:\t 天禄谈经\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['美德重红幸福门', '作业构三章', '道德五千', '四海奋人心', '功犹可迁', '仰百年师', '浓厚简约虚', '柳娜娜', '此味几人同', '撒豆成兵', '绿野寄仙踪', '难教白日闲', '梅韵贺新年', '文盲', '一街太平歌', '梧宫秋吴王愁', '赤水得玄珠', '栽培桃李成林', '心悟得真', '英雄是达人', '道理甚分明', '木栽门内闲', '庙略久论兵', '碧柳锁长亭', '风定水无波', '醉后赋离骚', '妙理贵躬行', '冬夜暖开心', '众号神君', '龙女牧羊', '新桥', '严师', '不言第一海胸襟', '府藏石铫图', '池浅韵牵波', '宛在岱中行', '天禄谈经', '寺与山争鲜', '酒醉好题诗', '冗鱼', '长庚', '碧浪皱红霞', '淡雅雪边梅', '初日临春虚', '福禄寿喜', '陕州人杰灵', '苔湿地刁皮', '鸟语落花山', '人我法皆空', '何防凿壁偷', '艺高大胆人', '一鳞', '起宏图', '时泰喜黎民', '月轮碾古今', '春入鸟能言', '蛇对赠君东海福', '大宴高轩', '白日奈我何', '单恋独予一江秋', '无肉也能行', '上寿可期', '苍苔', '学子话春浓']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: True\n",
|
|||
|
"liczba proponowanych wersów: 64\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.015625\n",
|
|||
|
"wyjaśnienie - największe wartości przewidywań\n",
|
|||
|
" indeks wartosc\n",
|
|||
|
"1113 1113 1.373200\n",
|
|||
|
"747 747 1.311217\n",
|
|||
|
"192 192 1.304834\n",
|
|||
|
"1101 1101 1.304543\n",
|
|||
|
"1188 1188 1.279343\n",
|
|||
|
"... ... ...\n",
|
|||
|
"303 303 0.921175\n",
|
|||
|
"1112 1112 0.918424\n",
|
|||
|
"1191 1191 0.917258\n",
|
|||
|
"509 509 0.903766\n",
|
|||
|
"298 298 0.898008\n",
|
|||
|
"\n",
|
|||
|
"[65 rows x 2 columns]\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 闻思修并重\n",
|
|||
|
"poprawny wers drugi:\t 人我法皆空\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['玉律始调阳', '游子自存心']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: False\n",
|
|||
|
"liczba proponowanych wersów: 2\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.0\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n",
|
|||
|
"wers pierwszy:\t\t 飞桥驾鹊天津阔\n",
|
|||
|
"poprawny wers drugi:\t 平沙戏马雨声干\n",
|
|||
|
"\n",
|
|||
|
"proponowane drugie wersy: ['草内多藏五步蛇', '后乐先忧范弟昆', '锦绣春归百姓家', '雾里青山画屏开', '映日桑榆重晚晴', '野渡闲游一叶舟', '日月同辉光景嫣', '酒醉不如伴月眠', '草木逢春年年生', '看三国志欲何为', '几管笛箫奏响春', '千树落花别样红', '地阔难及贪欲长', '大漠孤烟古道长', '然乎者一字乾坤', '南燕离巢北国春', '浅草雷门愧下关', '又何必三日闻香', '梦里飞花静闻香', '国运弥盛史弥远', '时急方须济世才', '五色龙溪抱江流', '动动脑筋动静无', '弹毕雅曲听和声', '春梦几枝与醉痴', '粉黛淡施十五光', '明月来时渚落霜', '北海波清映日黄', '映月二泉人世情', '风过泸州带酒香', '小觑浮名对酒歌', '不言第一海胸襟', '知耻明荣胸臆宽', '路远始于跬步间', '纵览清江高士怀', '皇城玉阙夕阳斜', '贞慧何辞驻翠颜', '落日栖霞赏故园', '大势所趋水如蓝', '白发无情忽上头', '夫再礼让妻再争', '无边相思似流云', '畅谈国事一腔情', '杨絮舞出风感觉', '室壁裂时蟢网缝', '月转疏枝过女墙', '七年对友队尤宏', '百年盟约好时光', '蔼峰亦寄诗仙情', '中散孤高故不凡', '马上蓝天宇拓宽', '美丽季节万里春', '部长铁男不定还', '慷慨悲歌魏晋风', '五井丰碑今日游', '载笔须来阙下游', '死后欣然上八仙', '兰桂齐芳福乐门', '饮酒月前独自愁', '廉政为民常山情', '北海泛舟携孔融', '四面荷花扑画船', '古韵古风誉古今', '梦远还托风导游', '处世何须带伪装', '杉木果林桃李荣', '春歌大地爱国情', '翠扇红衣十里香', '华夏农民开喜镰', '老虎苍蝇一起除', '蛇对赠君东海福', '红枣绿茶岭上香', '竹色四时也不移', '羊跃人欢艳阳春', '单恋独予一江秋', '水畔青田走马牛', '雅意如茶自在闲', '一地纸灰寂寞人', '归家且遂十年心', '紫燕翻飞柳泛青', '误将弟子入迷宫', '河朔膏腴古督亢', '花好焉无惬意时', '何必杀鸡笑野猴', '南粤万家景色新']\n",
|
|||
|
"czy poprawny wers jest pośród proponowanych wersów?: False\n",
|
|||
|
"liczba proponowanych wersów: 85\n",
|
|||
|
"\n",
|
|||
|
"wynik przyjętej metryki: 0.0\n",
|
|||
|
"wyjaśnienie - największe wartości przewidywań\n",
|
|||
|
" indeks wartosc\n",
|
|||
|
"896 896 1.700319\n",
|
|||
|
"632 632 1.542179\n",
|
|||
|
"871 871 1.496446\n",
|
|||
|
"1319 1319 1.451665\n",
|
|||
|
"1333 1333 1.371606\n",
|
|||
|
"... ... ...\n",
|
|||
|
"1375 1375 0.905215\n",
|
|||
|
"751 751 0.904408\n",
|
|||
|
"91 91 0.903730\n",
|
|||
|
"1356 1356 0.901601\n",
|
|||
|
"265 265 0.898689\n",
|
|||
|
"\n",
|
|||
|
"[86 rows x 2 columns]\n",
|
|||
|
"\n",
|
|||
|
"--------------------------------------------------\n",
|
|||
|
"\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"for indeks_wersu_pierwszego in wybrane_dane_testowe:\n",
|
|||
|
" wers_pierwszy = testowe[\"in\"][indeks_wersu_pierwszego]\n",
|
|||
|
" print(\"wers pierwszy:\\t\\t\", wers_pierwszy)\n",
|
|||
|
" poprawny_wers_drugi = testowe[\"out\"][indeks_wersu_pierwszego]\n",
|
|||
|
" print(\"poprawny wers drugi:\\t\", poprawny_wers_drugi)\n",
|
|||
|
" print()\n",
|
|||
|
"\n",
|
|||
|
" reprezentacja_wersu_pierwszego = x_test[indeks_wersu_pierwszego]\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego = []\n",
|
|||
|
" wartosci_przewidywan_wersu_drugiego = []\n",
|
|||
|
" for indeks_wersu_drugiego in range(len(y_test)):\n",
|
|||
|
" reprezentacja_wersu_drugiego = y_test[indeks_wersu_drugiego]\n",
|
|||
|
" wejscie_do_MLP = torch.cat((reprezentacja_wersu_pierwszego, reprezentacja_wersu_drugiego))\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego.append(indeks_wersu_drugiego)\n",
|
|||
|
" wartosci_przewidywan_wersu_drugiego.append(regresor.predict([wejscie_do_MLP])[0])\n",
|
|||
|
"\n",
|
|||
|
" pom_df = pandas.DataFrame({\"indeks\":mozliwe_indeksy_wersu_drugiego,\"wartosc\":wartosci_przewidywan_wersu_drugiego})\n",
|
|||
|
" proponowane_wersy = [testowe[\"out\"][i] for i in pom_df[\"indeks\"] if pom_df[\"wartosc\"][i]>=0.9]\n",
|
|||
|
"\n",
|
|||
|
" print(\"proponowane drugie wersy:\", proponowane_wersy)\n",
|
|||
|
" print(\"czy poprawny wers jest pośród proponowanych wersów?:\", poprawny_wers_drugi in proponowane_wersy)\n",
|
|||
|
" print(\"liczba proponowanych wersów:\", len(proponowane_wersy))\n",
|
|||
|
" print()\n",
|
|||
|
"\n",
|
|||
|
" print(\"wynik przyjętej metryki:\", jagosz_score(poprawny_wers_drugi, proponowane_wersy))\n",
|
|||
|
" if (len(proponowane_wersy)<1 or len(proponowane_wersy)>5):\n",
|
|||
|
" print(\"wyjaśnienie - największe wartości przewidywań\")\n",
|
|||
|
" print(pom_df.nlargest(len(proponowane_wersy)+1, \"wartosc\"))\n",
|
|||
|
" print()\n",
|
|||
|
" print(\"-\"*50)\n",
|
|||
|
" print()"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "Me3oxyE7zjRn"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"## MLPClassifier\n",
|
|||
|
"### Przyjęta metryka dla 1/100 zbioru testowego."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 92,
|
|||
|
"metadata": {
|
|||
|
"id": "2Sxf6bVyzjRn",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "35ce1b11-1b54-4e83-e7a3-e2c88176fe00"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"0.0005942200059422001\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"krotki = []\n",
|
|||
|
"czesc_zbioru_testowego, _ = train_test_split(x_test,test_size=0.95,random_state=42)\n",
|
|||
|
"for indeks_wersu_pierwszego in range(len(czesc_zbioru_testowego)):\n",
|
|||
|
" wers_pierwszy = testowe[\"in\"][indeks_wersu_pierwszego]\n",
|
|||
|
" poprawny_wers_drugi = testowe[\"out\"][indeks_wersu_pierwszego]\n",
|
|||
|
"\n",
|
|||
|
" reprezentacja_wersu_pierwszego = x_test[indeks_wersu_pierwszego]\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego = []\n",
|
|||
|
" wartosci_przewidywan_wersu_drugiego = []\n",
|
|||
|
" for indeks_wersu_drugiego in range(len(y_test)):\n",
|
|||
|
" reprezentacja_wersu_drugiego = y_test[indeks_wersu_drugiego]\n",
|
|||
|
" wejscie_do_MLP = torch.cat((reprezentacja_wersu_pierwszego, reprezentacja_wersu_drugiego))\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego.append(indeks_wersu_drugiego)\n",
|
|||
|
" if klasyfikator.predict([wejscie_do_MLP])[0] == 1:\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego.append(indeks_wersu_drugiego)\n",
|
|||
|
"\n",
|
|||
|
" proponowane_wersy = [testowe[\"out\"][i] for i in mozliwe_indeksy_wersu_drugiego]\n",
|
|||
|
"\n",
|
|||
|
" krotki.append((poprawny_wers_drugi,proponowane_wersy))\n",
|
|||
|
"\n",
|
|||
|
"print(jagosz_score_dla_zbioru(krotki))"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"metadata": {
|
|||
|
"id": "RzIw0t_szjRn"
|
|||
|
},
|
|||
|
"source": [
|
|||
|
"### Średnia metryk dla 1/100 zbioru testowego."
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 93,
|
|||
|
"metadata": {
|
|||
|
"id": "J8vC-zMRzjRn",
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"outputId": "28e160ea-15b4-40bc-a5ed-84f994219375"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"0.0005963998131847716\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"jagosz_scores=[]\n",
|
|||
|
"for indeks_wersu_pierwszego in range(len(czesc_zbioru_testowego)):\n",
|
|||
|
" wers_pierwszy = testowe[\"in\"][indeks_wersu_pierwszego]\n",
|
|||
|
" poprawny_wers_drugi = testowe[\"out\"][indeks_wersu_pierwszego]\n",
|
|||
|
"\n",
|
|||
|
" reprezentacja_wersu_pierwszego = x_test[indeks_wersu_pierwszego]\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego = []\n",
|
|||
|
" wartosci_przewidywan_wersu_drugiego = []\n",
|
|||
|
" for indeks_wersu_drugiego in range(len(y_test)):\n",
|
|||
|
" reprezentacja_wersu_drugiego = y_test[indeks_wersu_drugiego]\n",
|
|||
|
" wejscie_do_MLP = torch.cat((reprezentacja_wersu_pierwszego, reprezentacja_wersu_drugiego))\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego.append(indeks_wersu_drugiego)\n",
|
|||
|
" if klasyfikator.predict([wejscie_do_MLP])[0] == 1:\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego.append(indeks_wersu_drugiego)\n",
|
|||
|
"\n",
|
|||
|
" proponowane_wersy = [testowe[\"out\"][i] for i in mozliwe_indeksy_wersu_drugiego]\n",
|
|||
|
"\n",
|
|||
|
" jagosz_scores.append(jagosz_score(poprawny_wers_drugi,proponowane_wersy))\n",
|
|||
|
"\n",
|
|||
|
"print(numpy.mean(jagosz_scores))"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"source": [
|
|||
|
"## MLPClassifier\n",
|
|||
|
"### Przyjęta metryka dla 1/100 zbioru testowego."
|
|||
|
],
|
|||
|
"metadata": {
|
|||
|
"id": "eYaQZinPCzWK"
|
|||
|
}
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"source": [
|
|||
|
"krotki = []\n",
|
|||
|
"for indeks_wersu_pierwszego in range(len(czesc_zbioru_testowego)):\n",
|
|||
|
" wers_pierwszy = testowe[\"in\"][indeks_wersu_pierwszego]\n",
|
|||
|
" poprawny_wers_drugi = testowe[\"out\"][indeks_wersu_pierwszego]\n",
|
|||
|
"\n",
|
|||
|
" reprezentacja_wersu_pierwszego = x_test[indeks_wersu_pierwszego]\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego = []\n",
|
|||
|
" wartosci_przewidywan_wersu_drugiego = []\n",
|
|||
|
" for indeks_wersu_drugiego in range(len(y_test)):\n",
|
|||
|
" reprezentacja_wersu_drugiego = y_test[indeks_wersu_drugiego]\n",
|
|||
|
" wejscie_do_MLP = torch.cat((reprezentacja_wersu_pierwszego, reprezentacja_wersu_drugiego))\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego.append(indeks_wersu_drugiego)\n",
|
|||
|
" wartosci_przewidywan_wersu_drugiego.append(regresor.predict([wejscie_do_MLP])[0])\n",
|
|||
|
"\n",
|
|||
|
" pom_df = pandas.DataFrame({\"indeks\":mozliwe_indeksy_wersu_drugiego,\"wartosc\":wartosci_przewidywan_wersu_drugiego})\n",
|
|||
|
" proponowane_wersy = [testowe[\"out\"][i] for i in pom_df[\"indeks\"] if pom_df[\"wartosc\"][i]>=0.9]\n",
|
|||
|
"\n",
|
|||
|
" krotki.append((poprawny_wers_drugi,proponowane_wersy))\n",
|
|||
|
"\n",
|
|||
|
"print(jagosz_score_dla_zbioru(krotki))"
|
|||
|
],
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "oeD4oLI1BqRD",
|
|||
|
"outputId": "6662d3cb-69d2-41ba-a0c7-550a8acd32a9"
|
|||
|
},
|
|||
|
"execution_count": 98,
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"0.005119117936601693\n"
|
|||
|
]
|
|||
|
}
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "markdown",
|
|||
|
"source": [
|
|||
|
"### Średnia metryk dla 1/100 zbioru testowego."
|
|||
|
],
|
|||
|
"metadata": {
|
|||
|
"id": "osUCFsMUC582"
|
|||
|
}
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"source": [
|
|||
|
"jagosz_scores=[]\n",
|
|||
|
"for indeks_wersu_pierwszego in range(len(czesc_zbioru_testowego)):\n",
|
|||
|
" wers_pierwszy = testowe[\"in\"][indeks_wersu_pierwszego]\n",
|
|||
|
" poprawny_wers_drugi = testowe[\"out\"][indeks_wersu_pierwszego]\n",
|
|||
|
"\n",
|
|||
|
" reprezentacja_wersu_pierwszego = x_test[indeks_wersu_pierwszego]\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego = []\n",
|
|||
|
" wartosci_przewidywan_wersu_drugiego = []\n",
|
|||
|
" for indeks_wersu_drugiego in range(len(y_test)):\n",
|
|||
|
" reprezentacja_wersu_drugiego = y_test[indeks_wersu_drugiego]\n",
|
|||
|
" wejscie_do_MLP = torch.cat((reprezentacja_wersu_pierwszego, reprezentacja_wersu_drugiego))\n",
|
|||
|
" mozliwe_indeksy_wersu_drugiego.append(indeks_wersu_drugiego)\n",
|
|||
|
" wartosci_przewidywan_wersu_drugiego.append(regresor.predict([wejscie_do_MLP])[0])\n",
|
|||
|
"\n",
|
|||
|
" pom_df = pandas.DataFrame({\"indeks\":mozliwe_indeksy_wersu_drugiego,\"wartosc\":wartosci_przewidywan_wersu_drugiego})\n",
|
|||
|
" proponowane_wersy = [testowe[\"out\"][i] for i in pom_df[\"indeks\"] if pom_df[\"wartosc\"][i]>=0.9]\n",
|
|||
|
"\n",
|
|||
|
" jagosz_scores.append(jagosz_score(poprawny_wers_drugi,proponowane_wersy))\n",
|
|||
|
"\n",
|
|||
|
"print(numpy.mean(jagosz_scores))"
|
|||
|
],
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "BGjhbLM_CuB3",
|
|||
|
"outputId": "98df07ba-9c06-4b98-fe4e-789492ecccac"
|
|||
|
},
|
|||
|
"execution_count": 99,
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"0.011362779457775134\n"
|
|||
|
]
|
|||
|
}
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"source": [
|
|||
|
"print(len(czesc_zbioru_testowego))"
|
|||
|
],
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "DThPdUP2FfHT",
|
|||
|
"outputId": "ddc15db0-8cd8-4a6a-f3c6-634b2ad9f0ac"
|
|||
|
},
|
|||
|
"execution_count": 97,
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"74\n"
|
|||
|
]
|
|||
|
}
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"metadata": {
|
|||
|
"kernelspec": {
|
|||
|
"display_name": "Python 3",
|
|||
|
"name": "python3"
|
|||
|
},
|
|||
|
"language_info": {
|
|||
|
"codemirror_mode": {
|
|||
|
"name": "ipython",
|
|||
|
"version": 3
|
|||
|
},
|
|||
|
"file_extension": ".py",
|
|||
|
"mimetype": "text/x-python",
|
|||
|
"name": "python",
|
|||
|
"nbconvert_exporter": "python",
|
|||
|
"pygments_lexer": "ipython3",
|
|||
|
"version": "3.12.3"
|
|||
|
},
|
|||
|
"colab": {
|
|||
|
"provenance": [],
|
|||
|
"gpuType": "T4"
|
|||
|
},
|
|||
|
"accelerator": "GPU"
|
|||
|
},
|
|||
|
"nbformat": 4,
|
|||
|
"nbformat_minor": 0
|
|||
|
}
|