Laboratoria 1 - poprawki i uzupełnienia
This commit is contained in:
Paweł Skórzewski
parent
1f5c1d96d8
commit
8df2896694
@ -26,6 +26,44 @@
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"### Listy składane (*List comprehension*)"
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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": 2,
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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, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441, 484, 529, 576, 625, 676, 729, 784, 841, 900, 961, 1024, 1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849, 1936, 2025, 2116, 2209, 2304, 2401]\n"
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]
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}
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],
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"source": [
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"lista = [i**2 for i in range(50)]\n",
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"\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": 5,
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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: 0, 2: 4, 4: 16, 6: 36, 8: 64}\n"
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]
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}
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],
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"source": [
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"slownik = {k: k**2 for k in range(10) if k % 2 == 0}\n",
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"\n",
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"print(slownik)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -251,7 +289,7 @@
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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": 7,
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"metadata": {},
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"outputs": [
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{
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@ -280,7 +318,7 @@
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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": 8,
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"metadata": {},
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"outputs": [
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{
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@ -289,7 +327,7 @@
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"(3, 3)"
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]
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},
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"execution_count": 15,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -300,7 +338,7 @@
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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": 9,
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"metadata": {},
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"outputs": [
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{
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@ -309,7 +347,7 @@
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"array([12, 15, 18])"
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]
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},
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"execution_count": 16,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -320,22 +358,22 @@
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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": 11,
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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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"array([1, 4, 7])"
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]
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},
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"execution_count": 17,
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"execution_count": 11,
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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) # wektor złożony ze średnich elementów w każdym wierszu"
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"x.min(axis=1) # wektor złożony ze średnich elementów w każdym wierszu"
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]
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},
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{
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@ -347,7 +385,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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@ -356,7 +394,27 @@
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"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
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]
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},
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"execution_count": 18,
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"execution_count": 12,
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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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"np.array(list(range(10)))"
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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": 13,
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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, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -367,7 +425,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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@ -376,7 +434,7 @@
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"array([5, 6, 7, 8, 9])"
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]
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},
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"execution_count": 19,
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -387,22 +445,22 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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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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"data": {
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"text/plain": [
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"array([5. , 5.5, 6. , 6.5, 7. , 7.5, 8. , 8.5, 9. , 9.5])"
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"array([5, 7, 9])"
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]
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},
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"execution_count": 20,
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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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"np.arange(5, 10, 0.5)"
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"np.arange(5, 10, 2)"
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]
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},
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{
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@ -414,7 +472,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 18,
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"metadata": {
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"scrolled": true
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},
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@ -446,19 +504,20 @@
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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": 20,
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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. 0.11111111 0.22222222 0.33333333 0.44444444 0.55555556\n",
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" 0.66666667 0.77777778 0.88888889 1. ]\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, 1, 10)\n",
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"print(x)"
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]
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},
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@ -1036,7 +1095,7 @@
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}
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],
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"source": [
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"a[:10:2]"
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"a[:10:2] # elementy do 10., co drugi element"
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]
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},
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{
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@ -1056,7 +1115,7 @@
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}
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],
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"source": [
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"a[::-1]"
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"a[::-1] # wszytkie elementy tablicy `a`, ale w odwróconej kolejności"
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]
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},
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{
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@ -1068,7 +1127,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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@ -1079,7 +1138,7 @@
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" [ 8, 9, 10, 11]])"
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]
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},
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"execution_count": 46,
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1091,22 +1150,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"execution_count": null,
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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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"11"
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]
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},
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"execution_count": 47,
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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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"outputs": [],
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"source": [
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"x[2, 3]"
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"x[2, 3] # wiersz 2, kolumna 3"
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]
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},
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{
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@ -1126,7 +1174,7 @@
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}
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],
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"source": [
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"x[:, 1]"
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"x[:, 1] # kolumna 1"
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]
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},
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{
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@ -1146,7 +1194,7 @@
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}
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],
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"source": [
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"x[1, :]"
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"x[1, :] # wiersz 1"
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]
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},
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{
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@ -1167,7 +1215,7 @@
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}
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],
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"source": [
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"x[1:3, :]"
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"x[1:3, :] # wiersze od 1. włącznie do 3. wyłącznie"
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]
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},
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{
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@ -1337,6 +1385,7 @@
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}
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],
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"source": [
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"# Operacja `.flat` \"spłaszcza\" macierz\n",
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"for element in x.flat:\n",
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" print(element) "
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]
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@ -1365,7 +1414,7 @@
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}
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],
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"source": [
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"np.random.randint(0, 10, 5)"
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"np.random.randint(0, 10, 5) # Tablica złożona z 5 liczb całkowitych wylosowanych z zakresu od 0 do 10"
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]
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},
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{
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@ -1385,7 +1434,7 @@
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}
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],
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"source": [
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"np.random.normal(0, 1, 5) "
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"np.random.normal(0, 1, 5) # Tablica złożona z 5 liczb wylosowanych z rozkładu normalnego (0, 1)"
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]
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},
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{
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@ -1405,7 +1454,7 @@
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}
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],
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"source": [
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"np.random.uniform(0, 2, 5)"
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"np.random.uniform(0, 2, 5) # Tablica złożona z 5 liczb wylosowanych z rozkładu jednostajnego na przedziale (0, 1)"
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]
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},
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{
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@ -1421,6 +1470,8 @@
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"source": [
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"NumPy jest pakietem wykorzystywanym do obliczeń w dziedzinie algebry liniowej, co jeszcze szczególnie przydatne w uczeniu maszynowym. \n",
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"\n",
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"**Transpozycja** to operacja zamiany wierszy na kolumny i na odwrót.\n",
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"\n",
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"Wektor o wymiarach $1 \\times N$ \n",
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"$$\n",
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" x =\n",
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@ -1702,7 +1753,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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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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@ -1712,7 +1763,7 @@
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" [ 5, 5]])"
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]
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},
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"execution_count": 60,
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1724,7 +1775,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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{
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@ -1734,7 +1785,7 @@
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" [ 0.5, 0.4]])"
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]
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},
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"execution_count": 61,
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"execution_count": 28,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1744,6 +1795,26 @@
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"invA"
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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": 29,
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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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"[[-0.5 -0.2]\n",
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" [ 0.5 0.4]]\n"
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]
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}
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],
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"source": [
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"B = np.matrix(A)\n",
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"invB = B**-1\n",
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"print(invB)"
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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": 62,
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@ -2230,7 +2301,7 @@
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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": "3.8.3"
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"version": "3.7.6"
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},
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"livereveal": {
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"start_slideshow_at": "selected",
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