forked from filipg/aitech-eks-pub
move 03 solutions to solutions
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fdf0e512f2
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@ -309,22 +309,6 @@
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" pass"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"def word_to_index(word):\n",
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" vec = np.zeros(len(vocabulary))\n",
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" if word in vocabulary:\n",
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" idx = vocabulary.index(word)\n",
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" vec[idx] = 1\n",
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" else:\n",
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" vec[-1] = 1\n",
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" return vec"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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@ -368,16 +352,7 @@
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"def tf(document):\n",
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" document_vector = None\n",
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" for word in document:\n",
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" if document_vector is None:\n",
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" document_vector = word_to_index(word)\n",
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" else:\n",
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" document_vector += word_to_index(word)\n",
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" return document_vector"
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]
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"source": []
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},
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{
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"cell_type": "code",
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@ -495,14 +470,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def similarity(query, document):\n",
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" numerator = np.sum(query * document)\n",
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" denominator = np.sqrt(np.sum(query*query)) * np.sqrt(np.sum(document*document)) \n",
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" return numerator / denominator"
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" pass"
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]
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},
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{
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@ -1117,7 +1090,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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@ -1,11 +1,48 @@
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"def word_to_index(word):\n",
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" vec = np.zeros(len(vocabulary))\n",
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" if word in vocabulary:\n",
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" idx = vocabulary.index(word)\n",
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" vec[idx] = 1\n",
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" else:\n",
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" vec[-1] = 1\n",
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" return vec"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"def tf(document):\n",
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" document_vector = None\n",
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" for word in document:\n",
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" if document_vector is None:\n",
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" document_vector = word_to_index(word)\n",
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" else:\n",
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" document_vector += word_to_index(word)\n",
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" return document_vector"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def similarity(query, document):\n",
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" numerator = np.sum(query * document)\n",
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" denominator = np.sqrt(np.sum(query*query)) * np.sqrt(np.sum(document*document)) \n",
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" return numerator / denominator"
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]
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}
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],
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"metadata": {
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@ -24,7 +61,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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