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Roger Labbe committed 2020-04-26 18:42:30 -07:00
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00-Preface.ipynb
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@@ -27,8 +27,7 @@
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from __future__ import division, print_function"
"%matplotlib inline"
]
},
{
@@ -43,7 +42,7 @@
" <style>\n",
" .output_wrapper, .output {\n",
" height:auto !important;\n",
" max-height:10000px; \n",
" max-height:100000px; \n",
" }\n",
" .output_scroll {\n",
" box-shadow:none !important;\n",
@@ -211,7 +210,9 @@
"\n",
"First, some words about using Jupyter Notebooks with this book. This book is interactive. If you want to run code examples, and especially if you want to see animated plots, you will need to run the code cells. I cannot teach you everything about Jupyter Notebooks. However, a few things trip readers up. You can go to http://jupyter.org/ for detailed documentation.\n",
"\n",
"First, you must always run the topmost code cell, the one with the comment `#format the book`. It is directly above. This does not just set up formatting, which you might not care about, but it also loads some necessary modules and makes some global settings regarding plotting and printing. So, always run this cell unless you are just passively reading. The import from `__future__` helps Python 2.7 work like Python 3.X. Division of integers will return a `float` (`3/10 == 0.3`) instead of an `int` (`3/10 == 0`), and printing requires parens: `print(3)`, not `print 3`. The line\n",
"First, you must always run the topmost code cell, the one with the comment `#format the book`. It is directly above. This does not just set up formatting, which you might not care about, but it also loads some necessary modules and makes some global settings regarding plotting and printing. So, always run this cell unless you are just passively reading. \n",
"\n",
"The line\n",
"\n",
"```python\n",
"%matplotlib inline\n",
@@ -544,16 +545,37 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Python 3.5 introduced the `@` operator for matrix multiplication.\n",
"\n",
"```python\n",
"x @ x\n",
"\n",
"[[ 7.0 10.0]\n",
" [ 15.0 22.0]]\n",
"```\n",
"\n",
"This will only work if you are using Python 3.5+. So, as much as I prefer this notation to `np.dot(x, x)` I will not use it in this book."
"Python 3.5 introduced the `@` operator for matrix multiplication."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 7., 10.],\n",
" [15., 22.]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x @ x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This will only work if you are using Python 3.5+. This book requires 3.5 or later, so I will use it whenever I can. Note that the operator requires that both values are arrays. Hence, `x @ 3.` raises a ValueError whereas `np.dot(X, 3.)` works fine."
]
},
{
@@ -565,7 +587,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -602,7 +624,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -639,7 +661,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -649,7 +671,7 @@
" 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9])"
]
},
"execution_count": 15,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -660,7 +682,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 17,
"metadata": {
"scrolled": true
},
@@ -673,7 +695,7 @@
" 1.895, 2. ])"
]
},
"execution_count": 16,
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -691,24 +713,24 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x20620443860>]"
"[<matplotlib.lines.Line2D at 0x16536bfdcf8>]"
]
},
"execution_count": 17,
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 900x400 with 1 Axes>"
"<matplotlib.figure.Figure at 0x16536b9c160>"
]
},
"metadata": {},
@@ -733,14 +755,14 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 900x400 with 1 Axes>"
"<matplotlib.figure.Figure at 0x16536ab06a0>"
]
},
"metadata": {},
@@ -769,7 +791,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
@@ -787,7 +809,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -803,7 +825,7 @@
"source": [
"print(np.ones(10) / 10.)\n",
"print(np.array([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1]))\n",
"print(np.array([.1]*10))"
"print(np.array([.1] * 10))"
]
},
{
@@ -815,7 +837,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -949,7 +971,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.6.4"
}
},
"nbformat": 4,