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Morten Hjorth-Jensen
2021-10-07 09:02:53 +02:00
parent 0dab750863
commit c1b0b5fdc0
7 changed files with 18 additions and 18 deletions
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doc/pub/week40/html/._week40-bs012.html
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@@ -319,11 +319,11 @@ MathJax.Hub.Config({
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">import</span> SGDRegressor
n <span style="color: #666666">=</span> <span style="color: #666666">100</span>
n <span style="color: #666666">=</span> <span style="color: #666666">1000</span>
x <span style="color: #666666">=</span> <span style="color: #666666">2*</span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>rand(n,<span style="color: #666666">1</span>)
y <span style="color: #666666">=</span> <span style="color: #666666">4+3*</span>x<span style="color: #666666">+</span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randn(n,<span style="color: #666666">1</span>)
X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>c_[np<span style="color: #666666">.</span>ones((m,<span style="color: #666666">1</span>)), x]
X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>c_[np<span style="color: #666666">.</span>ones((n,<span style="color: #666666">1</span>)), x]
theta_linreg <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linalg<span style="color: #666666">.</span>inv(X<span style="color: #666666">.</span>T <span style="color: #666666">@</span> X) <span style="color: #666666">@</span> (X<span style="color: #666666">.</span>T <span style="color: #666666">@</span> y)
<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Own inversion&quot;</span>)
<span style="color: #008000">print</span>(theta_linreg)
@@ -364,7 +364,7 @@ theta <span style="color: #666666">=</span> np<span style="color: #666666">.</sp
random_index <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randint(m)
xi <span style="color: #666666">=</span> X[random_index:random_index<span style="color: #666666">+1</span>]
yi <span style="color: #666666">=</span> y[random_index:random_index<span style="color: #666666">+1</span>]
gradients <span style="color: #666666">=</span> (<span style="color: #666666">2.0/</span>m) <span style="color: #666666">*</span> xi<span style="color: #666666">.</span>T <span style="color: #666666">@</span> ((xi <span style="color: #666666">@</span> theta)<span style="color: #666666">-</span>yi)
gradients <span style="color: #666666">=</span> <span style="color: #666666">2.0*</span> xi<span style="color: #666666">.</span>T <span style="color: #666666">@</span> ((xi <span style="color: #666666">@</span> theta)<span style="color: #666666">-</span>yi)
eta <span style="color: #666666">=</span> learning_schedule(epoch<span style="color: #666666">*</span>m<span style="color: #666666">+</span>i)
theta <span style="color: #666666">=</span> theta <span style="color: #666666">-</span> eta<span style="color: #666666">*</span>gradients
<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;theta from own sdg&quot;</span>)
doc/pub/week40/html/week40-reveal.html
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@@ -436,11 +436,11 @@ We note that we have defined several hyperparameters. These are now the number o
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">import</span> SGDRegressor
n = <span style="color: #B452CD">100</span>
n = <span style="color: #B452CD">1000</span>
x = <span style="color: #B452CD">2</span>*np.random.rand(n,<span style="color: #B452CD">1</span>)
y = <span style="color: #B452CD">4</span>+<span style="color: #B452CD">3</span>*x+np.random.randn(n,<span style="color: #B452CD">1</span>)
X = np.c_[np.ones((m,<span style="color: #B452CD">1</span>)), x]
X = np.c_[np.ones((n,<span style="color: #B452CD">1</span>)), x]
theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Own inversion&quot;</span>)
<span style="color: #658b00">print</span>(theta_linreg)
@@ -481,7 +481,7 @@ theta = np.random.randn(<span style="color: #B452CD">2</span>,<span style="color
random_index = np.random.randint(m)
xi = X[random_index:random_index+<span style="color: #B452CD">1</span>]
yi = y[random_index:random_index+<span style="color: #B452CD">1</span>]
gradients = (<span style="color: #B452CD">2.0</span>/m) * xi.T @ ((xi @ theta)-yi)
gradients = <span style="color: #B452CD">2.0</span>* xi.T @ ((xi @ theta)-yi)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients
<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;theta from own sdg&quot;</span>)
doc/pub/week40/html/week40-solarized.html
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@@ -525,11 +525,11 @@ We note that we have defined several hyperparameters. These are now the number o
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">import</span> SGDRegressor
n = <span style="color: #B452CD">100</span>
n = <span style="color: #B452CD">1000</span>
x = <span style="color: #B452CD">2</span>*np.random.rand(n,<span style="color: #B452CD">1</span>)
y = <span style="color: #B452CD">4</span>+<span style="color: #B452CD">3</span>*x+np.random.randn(n,<span style="color: #B452CD">1</span>)
X = np.c_[np.ones((m,<span style="color: #B452CD">1</span>)), x]
X = np.c_[np.ones((n,<span style="color: #B452CD">1</span>)), x]
theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Own inversion&quot;</span>)
<span style="color: #658b00">print</span>(theta_linreg)
@@ -570,7 +570,7 @@ theta = np.random.randn(<span style="color: #B452CD">2</span>,<span style="color
random_index = np.random.randint(m)
xi = X[random_index:random_index+<span style="color: #B452CD">1</span>]
yi = y[random_index:random_index+<span style="color: #B452CD">1</span>]
gradients = (<span style="color: #B452CD">2.0</span>/m) * xi.T @ ((xi @ theta)-yi)
gradients = <span style="color: #B452CD">2.0</span>* xi.T @ ((xi @ theta)-yi)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients
<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;theta from own sdg&quot;</span>)
doc/pub/week40/html/week40.html
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@@ -530,11 +530,11 @@ We note that we have defined several hyperparameters. These are now the number o
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">import</span> SGDRegressor
n <span style="color: #666666">=</span> <span style="color: #666666">100</span>
n <span style="color: #666666">=</span> <span style="color: #666666">1000</span>
x <span style="color: #666666">=</span> <span style="color: #666666">2*</span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>rand(n,<span style="color: #666666">1</span>)
y <span style="color: #666666">=</span> <span style="color: #666666">4+3*</span>x<span style="color: #666666">+</span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randn(n,<span style="color: #666666">1</span>)
X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>c_[np<span style="color: #666666">.</span>ones((m,<span style="color: #666666">1</span>)), x]
X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>c_[np<span style="color: #666666">.</span>ones((n,<span style="color: #666666">1</span>)), x]
theta_linreg <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linalg<span style="color: #666666">.</span>inv(X<span style="color: #666666">.</span>T <span style="color: #666666">@</span> X) <span style="color: #666666">@</span> (X<span style="color: #666666">.</span>T <span style="color: #666666">@</span> y)
<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Own inversion&quot;</span>)
<span style="color: #008000">print</span>(theta_linreg)
@@ -575,7 +575,7 @@ theta <span style="color: #666666">=</span> np<span style="color: #666666">.</sp
random_index <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randint(m)
xi <span style="color: #666666">=</span> X[random_index:random_index<span style="color: #666666">+1</span>]
yi <span style="color: #666666">=</span> y[random_index:random_index<span style="color: #666666">+1</span>]
gradients <span style="color: #666666">=</span> (<span style="color: #666666">2.0/</span>m) <span style="color: #666666">*</span> xi<span style="color: #666666">.</span>T <span style="color: #666666">@</span> ((xi <span style="color: #666666">@</span> theta)<span style="color: #666666">-</span>yi)
gradients <span style="color: #666666">=</span> <span style="color: #666666">2.0*</span> xi<span style="color: #666666">.</span>T <span style="color: #666666">@</span> ((xi <span style="color: #666666">@</span> theta)<span style="color: #666666">-</span>yi)
eta <span style="color: #666666">=</span> learning_schedule(epoch<span style="color: #666666">*</span>m<span style="color: #666666">+</span>i)
theta <span style="color: #666666">=</span> theta <span style="color: #666666">-</span> eta<span style="color: #666666">*</span>gradients
<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;theta from own sdg&quot;</span>)
doc/pub/week40/ipynb/ipynb-week40-src.tar.gz
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doc/pub/week40/ipynb/week40.ipynb
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@@ -311,11 +311,11 @@
"import matplotlib.pyplot as plt\n",
"from sklearn.linear_model import SGDRegressor\n",
"\n",
"n = 100\n",
"n = 1000\n",
"x = 2*np.random.rand(n,1)\n",
"y = 4+3*x+np.random.randn(n,1)\n",
"\n",
"X = np.c_[np.ones((m,1)), x]\n",
"X = np.c_[np.ones((n,1)), x]\n",
"theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)\n",
"print(\"Own inversion\")\n",
"print(theta_linreg)\n",
@@ -356,7 +356,7 @@
" random_index = np.random.randint(m)\n",
" xi = X[random_index:random_index+1]\n",
" yi = y[random_index:random_index+1]\n",
" gradients = (2.0/m) * xi.T @ ((xi @ theta)-yi)\n",
" gradients = 2.0* xi.T @ ((xi @ theta)-yi)\n",
" eta = learning_schedule(epoch*m+i)\n",
" theta = theta - eta*gradients\n",
"print(\"theta from own sdg\")\n",
doc/src/week40/week40.do.txt
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@@ -236,11 +236,11 @@ import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import SGDRegressor
n = 100
n = 1000
x = 2*np.random.rand(n,1)
y = 4+3*x+np.random.randn(n,1)
X = np.c_[np.ones((m,1)), x]
X = np.c_[np.ones((n,1)), x]
theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)
print("Own inversion")
print(theta_linreg)
@@ -281,7 +281,7 @@ for epoch in range(n_epochs):
random_index = np.random.randint(m)
xi = X[random_index:random_index+1]
yi = y[random_index:random_index+1]
gradients = (2.0/m) * xi.T @ ((xi @ theta)-yi)
gradients = 2.0* xi.T @ ((xi @ theta)-yi)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients
print("theta from own sdg")