forked from pms/uczenie-maszynowe
Aktualizacja materiałów do laboratoriów 1
This commit is contained in:
Paweł Skórzewski
parent
d81fefa352
commit
e481384b66
@ -35,7 +35,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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@ -78,8 +78,7 @@
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],
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"source": [
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"zdanie = \"tracz tarł tarcicę tak takt w takt jak takt w takt tarcicę tartak tarł\"\n",
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"wyrazy = zdanie.split()\n",
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"dlugosci_wyrazow = [len(wyraz) for wyraz in wyrazy]\n",
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"dlugosci_wyrazow = [len(wyraz) for wyraz in zdanie.split()]\n",
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"\n",
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"print(dlugosci_wyrazow)\n"
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]
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@ -134,6 +133,25 @@
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"Wszystkie listy i krotki w Pythonie, w tym łańcuchy (które trakowane są jak krotki znaków), są indeksowane od 0:"
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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": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[4, 16, 36, 64, 100]\n",
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"[4, 16, 36, 64, 100]\n"
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]
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}
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],
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"source": [
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"print(lista)\n",
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"print(lista[:])"
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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": 11,
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@ -255,7 +273,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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@ -264,14 +282,15 @@
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"text": [
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"[[1 2 3]\n",
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" [4 5 6]\n",
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" [7 8 9]]\n"
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" [7 8 9]\n",
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" [2 5 8]]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
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"x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [2, 5, 8]])\n",
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"print(x)\n"
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]
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},
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@ -284,62 +303,55 @@
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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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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(3, 3)"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(4, 3)\n"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x.shape\n"
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"print(x.shape) # wymiary macierzy\n"
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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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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([12, 15, 18])"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[14 20 26]\n",
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"[ 6 15 24 15]\n"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x.sum(axis=0)\n"
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"print(x.sum(axis=0)) # suma liczb w każdej kolumnie\n",
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"print(x.sum(axis=1)) # suma liczb w każdym wierszu"
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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": 17,
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([2., 5., 8.])"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2. 5. 8. 5.]\n"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x.mean(axis=1)\n"
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"print(x.mean(axis=1)) # średnia liczb w każdym wierszu\n"
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]
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},
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{
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@ -450,19 +462,19 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 32,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0. 1.25 2.5 3.75 5. ]\n"
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"[0. 0.625 1.25 1.875 2.5 3.125 3.75 4.375 5. ]\n"
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]
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}
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],
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"source": [
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"x = np.linspace(0, 5, 5)\n",
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"x = np.linspace(0, 5, 9)\n",
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"print(x)\n"
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]
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},
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@ -623,14 +635,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 53,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2. 3. 4.]\n"
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"[3 4 5]\n",
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"[1. 1. 1.]\n",
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"[3. 4. 5.]\n"
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]
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}
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],
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@ -638,8 +652,10 @@
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"import numpy as np\n",
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"\n",
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"a = np.array([3, 4, 5])\n",
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"print(a)\n",
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"b = np.ones(3)\n",
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"print(a - b)\n"
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"print(b)\n",
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"print(a / b)\n"
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]
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},
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{
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@ -651,7 +667,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 39,
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"metadata": {},
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"outputs": [
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{
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@ -670,7 +686,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 40,
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"metadata": {},
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"outputs": [
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{
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@ -689,17 +705,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 41,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 1, 4],\n",
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" [ 9, 16]])"
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"array([[ 7, 10],\n",
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" [15, 22]])"
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]
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},
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"execution_count": 30,
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"execution_count": 41,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -708,6 +724,27 @@
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"a * b # mnożenie element po elemencie\n"
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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": 42,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 7, 10],\n",
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" [15, 22]])"
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]
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},
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"execution_count": 42,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"a @ b # mnożenie macierzowe"
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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": 31,
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@ -907,7 +944,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 44,
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"metadata": {},
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"outputs": [
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{
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@ -916,7 +953,7 @@
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"16"
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]
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},
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"execution_count": 39,
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"execution_count": 44,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1128,7 +1165,7 @@
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}
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],
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"source": [
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"x[:, 1]\n"
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"x[:, 1] # kolumna nr 1\n"
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]
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},
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{
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@ -1148,7 +1185,7 @@
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}
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],
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"source": [
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"x[1, :]\n"
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"x[1, :] # wiersz nr 1\n"
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]
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},
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{
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@ -1352,16 +1389,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"execution_count": 48,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([2, 0, 7, 3, 5])"
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"array([1, 3, 3, 1, 2])"
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]
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},
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"execution_count": 50,
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"execution_count": 48,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1372,16 +1409,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"execution_count": 52,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([-0.7907838 , -0.65971486, 0.0375355 , 2.00045956, 0.32631216])"
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"array([ 2.25701199, -0.62666283, -0.58260693, 0.91053811, -0.12398967])"
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]
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},
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"execution_count": 51,
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"execution_count": 52,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1392,16 +1429,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"execution_count": 50,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1.50130054, 1.20710594, 0.45451505, 0.70098876, 0.90371663])"
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"array([0.64188687, 1.98379682, 0.4690363 , 1.26967692, 0.84376779])"
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]
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},
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"execution_count": 52,
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"execution_count": 50,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -2220,7 +2257,7 @@
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"metadata": {
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"celltoolbar": "Slideshow",
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"kernelspec": {
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"display_name": "Python 3.10.6 64-bit",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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