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more irritating typos

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Morten Hjorth-Jensen
2021-09-23 09:09:42 +02:00
parent c76f1d1e5a
commit d69f43cbaf
7 changed files with 42 additions and 42 deletions
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doc/pub/week38/html/._week38-bs014.html
+7 -7
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@@ -460,11 +460,11 @@ X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>z
beta <span style="color: #666666">=</span> fit_beta(X, y)
<span style="color: #408080; font-style: italic"># Intercept is included in the design matrix</span>
clf <span style="color: #666666">=</span> LinearRegression(fit_intercept<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">False</span>)<span style="color: #666666">.</span>fit(X, y)
skl <span style="color: #666666">=</span> LinearRegression(fit_intercept<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">False</span>)<span style="color: #666666">.</span>fit(X, y)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;True beta: </span><span style="color: #BB6688; font-weight: bold">{</span>true_beta<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Fitted beta: </span><span style="color: #BB6688; font-weight: bold">{</span>beta<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Sklearn fitted beta: </span><span style="color: #BB6688; font-weight: bold">{</span>clf<span style="color: #666666">.</span>coef_<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Sklearn fitted beta: </span><span style="color: #BB6688; font-weight: bold">{</span>skl<span style="color: #666666">.</span>coef_<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
ypredictOwn <span style="color: #666666">=</span> X <span style="color: #666666">@</span> beta
ypredictSKL <span style="color: #666666">=</span> skl<span style="color: #666666">.</span>predict(X)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with intercept column&quot;</span>)
@@ -476,7 +476,7 @@ ypredictSKL <span style="color: #666666">=</span> skl<span style="color: #666666
plt<span style="color: #666666">.</span>figure()
plt<span style="color: #666666">.</span>scatter(x, y, label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Data&quot;</span>)
plt<span style="color: #666666">.</span>plot(x, X <span style="color: #666666">@</span> beta, label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Fit&quot;</span>)
plt<span style="color: #666666">.</span>plot(x, clf<span style="color: #666666">.</span>predict(X), label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Sklearn (fit_intercept=False)&quot;</span>)
plt<span style="color: #666666">.</span>plot(x, skl<span style="color: #666666">.</span>predict(X), label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Sklearn (fit_intercept=False)&quot;</span>)
<span style="color: #408080; font-style: italic"># Do not include the intercept in the design matrix</span>
@@ -486,7 +486,7 @@ X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>z
X[:, p] <span style="color: #666666">=</span> x <span style="color: #666666">**</span> (p <span style="color: #666666">+</span> <span style="color: #666666">1</span>)
<span style="color: #408080; font-style: italic"># Intercept is not included in the design matrix</span>
clf <span style="color: #666666">=</span> LinearRegression(fit_intercept<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)<span style="color: #666666">.</span>fit(X, y)
skl <span style="color: #666666">=</span> LinearRegression(fit_intercept<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)<span style="color: #666666">.</span>fit(X, y)
<span style="color: #408080; font-style: italic"># Use centered values for X and y when computing coefficients</span>
y_offset <span style="color: #666666">=</span> np<span style="color: #666666">.</span>average(y, axis<span style="color: #666666">=0</span>)
@@ -497,8 +497,8 @@ intercept <span style="color: #666666">=</span> np<span style="color: #666666">.
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Manual intercept: </span><span style="color: #BB6688; font-weight: bold">{</span>intercept<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Fitted beta (wiothout intercept): </span><span style="color: #BB6688; font-weight: bold">{</span>beta<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Sklearn intercept: </span><span style="color: #BB6688; font-weight: bold">{</span>clf<span style="color: #666666">.</span>intercept_<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Sklearn fitted beta (without intercept): </span><span style="color: #BB6688; font-weight: bold">{</span>clf<span style="color: #666666">.</span>coef_<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Sklearn intercept: </span><span style="color: #BB6688; font-weight: bold">{</span>skl<span style="color: #666666">.</span>intercept_<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Sklearn fitted beta (without intercept): </span><span style="color: #BB6688; font-weight: bold">{</span>skl<span style="color: #666666">.</span>coef_<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
ypredictOwn <span style="color: #666666">=</span> X <span style="color: #666666">@</span> beta
ypredictSKL <span style="color: #666666">=</span> skl<span style="color: #666666">.</span>predict(X)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with Manual intercept&quot;</span>)
@@ -507,7 +507,7 @@ ypredictSKL <span style="color: #666666">=</span> skl<span style="color: #666666
<span style="color: #008000">print</span>(MSE(y,ypredictSKL))
plt<span style="color: #666666">.</span>plot(x, X <span style="color: #666666">@</span> beta <span style="color: #666666">+</span> intercept, <span style="color: #BA2121">&quot;--&quot;</span>, label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Fit (manual intercept)&quot;</span>)
plt<span style="color: #666666">.</span>plot(x, clf<span style="color: #666666">.</span>predict(X), <span style="color: #BA2121">&quot;--&quot;</span>, label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Sklearn (fit_intercept=True)&quot;</span>)
plt<span style="color: #666666">.</span>plot(x, skl<span style="color: #666666">.</span>predict(X), <span style="color: #BA2121">&quot;--&quot;</span>, label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Sklearn (fit_intercept=True)&quot;</span>)
plt<span style="color: #666666">.</span>grid()
plt<span style="color: #666666">.</span>legend()
doc/pub/week38/html/week38-reveal.html
+7 -7
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@@ -713,11 +713,11 @@ X = np.zeros((<span style="color: #658b00">len</span>(x), degree))
beta = fit_beta(X, y)
<span style="color: #228B22"># Intercept is included in the design matrix</span>
clf = LinearRegression(fit_intercept=<span style="color: #8B008B; font-weight: bold">False</span>).fit(X, y)
skl = LinearRegression(fit_intercept=<span style="color: #8B008B; font-weight: bold">False</span>).fit(X, y)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;True beta: {</span>true_beta<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Fitted beta: {</span>beta<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn fitted beta: {</span>clf.coef_<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn fitted beta: {</span>skl.coef_<span style="color: #CD5555">}&quot;</span>)
ypredictOwn = X @ beta
ypredictSKL = skl.predict(X)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with intercept column&quot;</span>)
@@ -729,7 +729,7 @@ ypredictSKL = skl.predict(X)
plt.figure()
plt.scatter(x, y, label=<span style="color: #CD5555">&quot;Data&quot;</span>)
plt.plot(x, X @ beta, label=<span style="color: #CD5555">&quot;Fit&quot;</span>)
plt.plot(x, clf.predict(X), label=<span style="color: #CD5555">&quot;Sklearn (fit_intercept=False)&quot;</span>)
plt.plot(x, skl.predict(X), label=<span style="color: #CD5555">&quot;Sklearn (fit_intercept=False)&quot;</span>)
<span style="color: #228B22"># Do not include the intercept in the design matrix</span>
@@ -739,7 +739,7 @@ X = np.zeros((<span style="color: #658b00">len</span>(x), degree - <span style="
X[:, p] = x ** (p + <span style="color: #B452CD">1</span>)
<span style="color: #228B22"># Intercept is not included in the design matrix</span>
clf = LinearRegression(fit_intercept=<span style="color: #8B008B; font-weight: bold">True</span>).fit(X, y)
skl = LinearRegression(fit_intercept=<span style="color: #8B008B; font-weight: bold">True</span>).fit(X, y)
<span style="color: #228B22"># Use centered values for X and y when computing coefficients</span>
y_offset = np.average(y, axis=<span style="color: #B452CD">0</span>)
@@ -750,8 +750,8 @@ intercept = np.mean(y_offset - X_offset @ beta)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Manual intercept: {</span>intercept<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Fitted beta (wiothout intercept): {</span>beta<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn intercept: {</span>clf.intercept_<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn fitted beta (without intercept): {</span>clf.coef_<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn intercept: {</span>skl.intercept_<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn fitted beta (without intercept): {</span>skl.coef_<span style="color: #CD5555">}&quot;</span>)
ypredictOwn = X @ beta
ypredictSKL = skl.predict(X)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with Manual intercept&quot;</span>)
@@ -760,7 +760,7 @@ ypredictSKL = skl.predict(X)
<span style="color: #658b00">print</span>(MSE(y,ypredictSKL))
plt.plot(x, X @ beta + intercept, <span style="color: #CD5555">&quot;--&quot;</span>, label=<span style="color: #CD5555">&quot;Fit (manual intercept)&quot;</span>)
plt.plot(x, clf.predict(X), <span style="color: #CD5555">&quot;--&quot;</span>, label=<span style="color: #CD5555">&quot;Sklearn (fit_intercept=True)&quot;</span>)
plt.plot(x, skl.predict(X), <span style="color: #CD5555">&quot;--&quot;</span>, label=<span style="color: #CD5555">&quot;Sklearn (fit_intercept=True)&quot;</span>)
plt.grid()
plt.legend()
doc/pub/week38/html/week38-solarized.html
+7 -7
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@@ -847,11 +847,11 @@ X = np.zeros((<span style="color: #658b00">len</span>(x), degree))
beta = fit_beta(X, y)
<span style="color: #228B22"># Intercept is included in the design matrix</span>
clf = LinearRegression(fit_intercept=<span style="color: #8B008B; font-weight: bold">False</span>).fit(X, y)
skl = LinearRegression(fit_intercept=<span style="color: #8B008B; font-weight: bold">False</span>).fit(X, y)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;True beta: {</span>true_beta<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Fitted beta: {</span>beta<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn fitted beta: {</span>clf.coef_<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn fitted beta: {</span>skl.coef_<span style="color: #CD5555">}&quot;</span>)
ypredictOwn = X @ beta
ypredictSKL = skl.predict(X)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with intercept column&quot;</span>)
@@ -863,7 +863,7 @@ ypredictSKL = skl.predict(X)
plt.figure()
plt.scatter(x, y, label=<span style="color: #CD5555">&quot;Data&quot;</span>)
plt.plot(x, X @ beta, label=<span style="color: #CD5555">&quot;Fit&quot;</span>)
plt.plot(x, clf.predict(X), label=<span style="color: #CD5555">&quot;Sklearn (fit_intercept=False)&quot;</span>)
plt.plot(x, skl.predict(X), label=<span style="color: #CD5555">&quot;Sklearn (fit_intercept=False)&quot;</span>)
<span style="color: #228B22"># Do not include the intercept in the design matrix</span>
@@ -873,7 +873,7 @@ X = np.zeros((<span style="color: #658b00">len</span>(x), degree - <span style="
X[:, p] = x ** (p + <span style="color: #B452CD">1</span>)
<span style="color: #228B22"># Intercept is not included in the design matrix</span>
clf = LinearRegression(fit_intercept=<span style="color: #8B008B; font-weight: bold">True</span>).fit(X, y)
skl = LinearRegression(fit_intercept=<span style="color: #8B008B; font-weight: bold">True</span>).fit(X, y)
<span style="color: #228B22"># Use centered values for X and y when computing coefficients</span>
y_offset = np.average(y, axis=<span style="color: #B452CD">0</span>)
@@ -884,8 +884,8 @@ intercept = np.mean(y_offset - X_offset @ beta)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Manual intercept: {</span>intercept<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Fitted beta (wiothout intercept): {</span>beta<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn intercept: {</span>clf.intercept_<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn fitted beta (without intercept): {</span>clf.coef_<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn intercept: {</span>skl.intercept_<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn fitted beta (without intercept): {</span>skl.coef_<span style="color: #CD5555">}&quot;</span>)
ypredictOwn = X @ beta
ypredictSKL = skl.predict(X)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with Manual intercept&quot;</span>)
@@ -894,7 +894,7 @@ ypredictSKL = skl.predict(X)
<span style="color: #658b00">print</span>(MSE(y,ypredictSKL))
plt.plot(x, X @ beta + intercept, <span style="color: #CD5555">&quot;--&quot;</span>, label=<span style="color: #CD5555">&quot;Fit (manual intercept)&quot;</span>)
plt.plot(x, clf.predict(X), <span style="color: #CD5555">&quot;--&quot;</span>, label=<span style="color: #CD5555">&quot;Sklearn (fit_intercept=True)&quot;</span>)
plt.plot(x, skl.predict(X), <span style="color: #CD5555">&quot;--&quot;</span>, label=<span style="color: #CD5555">&quot;Sklearn (fit_intercept=True)&quot;</span>)
plt.grid()
plt.legend()
doc/pub/week38/html/week38.html
+7 -7
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@@ -852,11 +852,11 @@ X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>z
beta <span style="color: #666666">=</span> fit_beta(X, y)
<span style="color: #408080; font-style: italic"># Intercept is included in the design matrix</span>
clf <span style="color: #666666">=</span> LinearRegression(fit_intercept<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">False</span>)<span style="color: #666666">.</span>fit(X, y)
skl <span style="color: #666666">=</span> LinearRegression(fit_intercept<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">False</span>)<span style="color: #666666">.</span>fit(X, y)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;True beta: </span><span style="color: #BB6688; font-weight: bold">{</span>true_beta<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Fitted beta: </span><span style="color: #BB6688; font-weight: bold">{</span>beta<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Sklearn fitted beta: </span><span style="color: #BB6688; font-weight: bold">{</span>clf<span style="color: #666666">.</span>coef_<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Sklearn fitted beta: </span><span style="color: #BB6688; font-weight: bold">{</span>skl<span style="color: #666666">.</span>coef_<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
ypredictOwn <span style="color: #666666">=</span> X <span style="color: #666666">@</span> beta
ypredictSKL <span style="color: #666666">=</span> skl<span style="color: #666666">.</span>predict(X)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with intercept column&quot;</span>)
@@ -868,7 +868,7 @@ ypredictSKL <span style="color: #666666">=</span> skl<span style="color: #666666
plt<span style="color: #666666">.</span>figure()
plt<span style="color: #666666">.</span>scatter(x, y, label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Data&quot;</span>)
plt<span style="color: #666666">.</span>plot(x, X <span style="color: #666666">@</span> beta, label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Fit&quot;</span>)
plt<span style="color: #666666">.</span>plot(x, clf<span style="color: #666666">.</span>predict(X), label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Sklearn (fit_intercept=False)&quot;</span>)
plt<span style="color: #666666">.</span>plot(x, skl<span style="color: #666666">.</span>predict(X), label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Sklearn (fit_intercept=False)&quot;</span>)
<span style="color: #408080; font-style: italic"># Do not include the intercept in the design matrix</span>
@@ -878,7 +878,7 @@ X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>z
X[:, p] <span style="color: #666666">=</span> x <span style="color: #666666">**</span> (p <span style="color: #666666">+</span> <span style="color: #666666">1</span>)
<span style="color: #408080; font-style: italic"># Intercept is not included in the design matrix</span>
clf <span style="color: #666666">=</span> LinearRegression(fit_intercept<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)<span style="color: #666666">.</span>fit(X, y)
skl <span style="color: #666666">=</span> LinearRegression(fit_intercept<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)<span style="color: #666666">.</span>fit(X, y)
<span style="color: #408080; font-style: italic"># Use centered values for X and y when computing coefficients</span>
y_offset <span style="color: #666666">=</span> np<span style="color: #666666">.</span>average(y, axis<span style="color: #666666">=0</span>)
@@ -889,8 +889,8 @@ intercept <span style="color: #666666">=</span> np<span style="color: #666666">.
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Manual intercept: </span><span style="color: #BB6688; font-weight: bold">{</span>intercept<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Fitted beta (wiothout intercept): </span><span style="color: #BB6688; font-weight: bold">{</span>beta<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Sklearn intercept: </span><span style="color: #BB6688; font-weight: bold">{</span>clf<span style="color: #666666">.</span>intercept_<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Sklearn fitted beta (without intercept): </span><span style="color: #BB6688; font-weight: bold">{</span>clf<span style="color: #666666">.</span>coef_<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Sklearn intercept: </span><span style="color: #BB6688; font-weight: bold">{</span>skl<span style="color: #666666">.</span>intercept_<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;Sklearn fitted beta (without intercept): </span><span style="color: #BB6688; font-weight: bold">{</span>skl<span style="color: #666666">.</span>coef_<span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">&quot;</span>)
ypredictOwn <span style="color: #666666">=</span> X <span style="color: #666666">@</span> beta
ypredictSKL <span style="color: #666666">=</span> skl<span style="color: #666666">.</span>predict(X)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with Manual intercept&quot;</span>)
@@ -899,7 +899,7 @@ ypredictSKL <span style="color: #666666">=</span> skl<span style="color: #666666
<span style="color: #008000">print</span>(MSE(y,ypredictSKL))
plt<span style="color: #666666">.</span>plot(x, X <span style="color: #666666">@</span> beta <span style="color: #666666">+</span> intercept, <span style="color: #BA2121">&quot;--&quot;</span>, label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Fit (manual intercept)&quot;</span>)
plt<span style="color: #666666">.</span>plot(x, clf<span style="color: #666666">.</span>predict(X), <span style="color: #BA2121">&quot;--&quot;</span>, label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Sklearn (fit_intercept=True)&quot;</span>)
plt<span style="color: #666666">.</span>plot(x, skl<span style="color: #666666">.</span>predict(X), <span style="color: #BA2121">&quot;--&quot;</span>, label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Sklearn (fit_intercept=True)&quot;</span>)
plt<span style="color: #666666">.</span>grid()
plt<span style="color: #666666">.</span>legend()
doc/pub/week38/ipynb/ipynb-week38-src.tar.gz
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doc/pub/week38/ipynb/week38.ipynb
+7 -7
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@@ -776,11 +776,11 @@
"beta = fit_beta(X, y)\n",
"\n",
"# Intercept is included in the design matrix\n",
"clf = LinearRegression(fit_intercept=False).fit(X, y)\n",
"skl = LinearRegression(fit_intercept=False).fit(X, y)\n",
"\n",
"print(f\"True beta: {true_beta}\")\n",
"print(f\"Fitted beta: {beta}\")\n",
"print(f\"Sklearn fitted beta: {clf.coef_}\")\n",
"print(f\"Sklearn fitted beta: {skl.coef_}\")\n",
"ypredictOwn = X @ beta\n",
"ypredictSKL = skl.predict(X)\n",
"print(f\"MSE with intercept column\")\n",
@@ -792,7 +792,7 @@
"plt.figure()\n",
"plt.scatter(x, y, label=\"Data\")\n",
"plt.plot(x, X @ beta, label=\"Fit\")\n",
"plt.plot(x, clf.predict(X), label=\"Sklearn (fit_intercept=False)\")\n",
"plt.plot(x, skl.predict(X), label=\"Sklearn (fit_intercept=False)\")\n",
"\n",
"\n",
"# Do not include the intercept in the design matrix\n",
@@ -802,7 +802,7 @@
" X[:, p] = x ** (p + 1)\n",
"\n",
"# Intercept is not included in the design matrix\n",
"clf = LinearRegression(fit_intercept=True).fit(X, y)\n",
"skl = LinearRegression(fit_intercept=True).fit(X, y)\n",
"\n",
"# Use centered values for X and y when computing coefficients\n",
"y_offset = np.average(y, axis=0)\n",
@@ -813,8 +813,8 @@
"\n",
"print(f\"Manual intercept: {intercept}\")\n",
"print(f\"Fitted beta (wiothout intercept): {beta}\")\n",
"print(f\"Sklearn intercept: {clf.intercept_}\")\n",
"print(f\"Sklearn fitted beta (without intercept): {clf.coef_}\")\n",
"print(f\"Sklearn intercept: {skl.intercept_}\")\n",
"print(f\"Sklearn fitted beta (without intercept): {skl.coef_}\")\n",
"ypredictOwn = X @ beta\n",
"ypredictSKL = skl.predict(X)\n",
"print(f\"MSE with Manual intercept\")\n",
@@ -823,7 +823,7 @@
"print(MSE(y,ypredictSKL))\n",
"\n",
"plt.plot(x, X @ beta + intercept, \"--\", label=\"Fit (manual intercept)\")\n",
"plt.plot(x, clf.predict(X), \"--\", label=\"Sklearn (fit_intercept=True)\")\n",
"plt.plot(x, skl.predict(X), \"--\", label=\"Sklearn (fit_intercept=True)\")\n",
"plt.grid()\n",
"plt.legend()\n",
"\n",
doc/src/week38/week38.do.txt
+7 -7
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@@ -492,11 +492,11 @@ for p in range(degree):
beta = fit_beta(X, y)
# Intercept is included in the design matrix
clf = LinearRegression(fit_intercept=False).fit(X, y)
skl = LinearRegression(fit_intercept=False).fit(X, y)
print(f"True beta: {true_beta}")
print(f"Fitted beta: {beta}")
print(f"Sklearn fitted beta: {clf.coef_}")
print(f"Sklearn fitted beta: {skl.coef_}")
ypredictOwn = X @ beta
ypredictSKL = skl.predict(X)
print(f"MSE with intercept column")
@@ -508,7 +508,7 @@ print(MSE(y,ypredictSKL))
plt.figure()
plt.scatter(x, y, label="Data")
plt.plot(x, X @ beta, label="Fit")
plt.plot(x, clf.predict(X), label="Sklearn (fit_intercept=False)")
plt.plot(x, skl.predict(X), label="Sklearn (fit_intercept=False)")
# Do not include the intercept in the design matrix
@@ -518,7 +518,7 @@ for p in range(degree - 1):
X[:, p] = x ** (p + 1)
# Intercept is not included in the design matrix
clf = LinearRegression(fit_intercept=True).fit(X, y)
skl = LinearRegression(fit_intercept=True).fit(X, y)
# Use centered values for X and y when computing coefficients
y_offset = np.average(y, axis=0)
@@ -529,8 +529,8 @@ intercept = np.mean(y_offset - X_offset @ beta)
print(f"Manual intercept: {intercept}")
print(f"Fitted beta (wiothout intercept): {beta}")
print(f"Sklearn intercept: {clf.intercept_}")
print(f"Sklearn fitted beta (without intercept): {clf.coef_}")
print(f"Sklearn intercept: {skl.intercept_}")
print(f"Sklearn fitted beta (without intercept): {skl.coef_}")
ypredictOwn = X @ beta
ypredictSKL = skl.predict(X)
print(f"MSE with Manual intercept")
@@ -539,7 +539,7 @@ print(f"MSE with Sklearn intercept")
print(MSE(y,ypredictSKL))
plt.plot(x, X @ beta + intercept, "--", label="Fit (manual intercept)")
plt.plot(x, clf.predict(X), "--", label="Sklearn (fit_intercept=True)")
plt.plot(x, skl.predict(X), "--", label="Sklearn (fit_intercept=True)")
plt.grid()
plt.legend()