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Section 6 exercises are done

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section_6_appendix/28_exercise_2.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from random import randrange\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"X = np.random.random(1000)\n",
"Y = [0]*1000"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"for i in range(1000):\n",
" for j in range(1000):\n",
" Y[i] += X[randrange(1000)]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(Y, bins=100)\n",
"plt.show()"
]
}
],
"metadata": {
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section_6_appendix/28_exercise_5.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
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{
"data": {
"text/plain": [
"array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]])"
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"metadata": {},
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}
],
"source": [
"I3 = np.eye(3)\n",
"I3"
]
},
{
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"source": [
"A = np.array([[0.3, 0.6, 0.1],\n",
" [0.5, 0.2, 0.3],\n",
" [0.4, 0.1, 0.5]])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def is_symmetric_automatic(matrix):\n",
" return (matrix == matrix.T).all()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"is_symmetric_automatic(A)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"is_symmetric_automatic(I3)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def is_symmetric_manual(matrix):\n",
" matrix_T = np.zeros((len(matrix), len(matrix[0])))\n",
" for i in range(len(matrix)):\n",
" for j in range(len(matrix[i])):\n",
" matrix_T[i,j] = matrix[j,i]\n",
" return(matrix_T == matrix).all()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
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"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"is_symmetric_manual(A)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"is_symmetric_automatic(I3)"
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],
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section_6_appendix/28_exercise_6.ipynb 0 → 100644
This source diff could not be displayed because it is too large. You can view the blob instead.
section_6_appendix/28_exercise_9.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"b = np.random.uniform(-1, 1, (1000, 2))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
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"text/plain": [
" 0 1\n",
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"source": [
"df = pd.DataFrame(b)\n",
"df.head()"
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},
{
"cell_type": "code",
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"metadata": {},
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"source": [
"csv = df.to_csv(path_or_buf=\"data.csv\")"
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}
],
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