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Morten Hjorth-Jensen
2021-09-23 09:06:59 +02:00
parent ca96a8b4dd
commit c76f1d1e5a
7 changed files with 48 additions and 48 deletions
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doc/pub/week38/html/._week38-bs014.html
+8 -8
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@@ -467,10 +467,10 @@ clf <span style="color: #666666">=</span> LinearRegression(fit_intercept<span st
<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>)
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: </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>(MSE(y,ypredictOwn)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with intercept column from SKL: </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>(MSE(y,ypredictSKL)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with intercept column&quot;</span>)
<span style="color: #008000">print</span>(MSE(y,ypredictOwn))
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with intercept column from SKL&quot;</span>)
<span style="color: #008000">print</span>(MSE(y,ypredictSKL))
plt<span style="color: #666666">.</span>figure()
@@ -501,10 +501,10 @@ intercept <span style="color: #666666">=</span> np<span style="color: #666666">.
<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>)
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: </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>(MSE(y,ypredictOwn)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with 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>(MSE(y,ypredictSKL)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with Manual intercept&quot;</span>)
<span style="color: #008000">print</span>(MSE(y,ypredictOwn))
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with Sklearn intercept&quot;</span>)
<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>)
doc/pub/week38/html/week38-reveal.html
+8 -8
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@@ -720,10 +720,10 @@ clf = LinearRegression(fit_intercept=<span style="color: #8B008B; font-weight: b
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn fitted beta: {</span>clf.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: {</span>intercept<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictOwn)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with intercept column from SKL: {</span>intercept<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictSKL)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with intercept column&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictOwn))
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with intercept column from SKL&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictSKL))
plt.figure()
@@ -754,10 +754,10 @@ intercept = np.mean(y_offset - X_offset @ beta)
<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>)
ypredictOwn = X @ beta
ypredictSKL = skl.predict(X)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with Manual intercept: {</span>intercept<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictOwn)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with Sklearn intercept: {</span>clf.intercept_<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictSKL)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with Manual intercept&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictOwn))
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with Sklearn intercept&quot;</span>)
<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>)
doc/pub/week38/html/week38-solarized.html
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@@ -854,10 +854,10 @@ clf = LinearRegression(fit_intercept=<span style="color: #8B008B; font-weight: b
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;Sklearn fitted beta: {</span>clf.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: {</span>intercept<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictOwn)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with intercept column from SKL: {</span>intercept<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictSKL)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with intercept column&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictOwn))
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with intercept column from SKL&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictSKL))
plt.figure()
@@ -888,10 +888,10 @@ intercept = np.mean(y_offset - X_offset @ beta)
<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>)
ypredictOwn = X @ beta
ypredictSKL = skl.predict(X)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with Manual intercept: {</span>intercept<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictOwn)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with Sklearn intercept: {</span>clf.intercept_<span style="color: #CD5555">}&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictSKL)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with Manual intercept&quot;</span>)
<span style="color: #658b00">print</span>(MSE(y,ypredictOwn))
<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;MSE with Sklearn intercept&quot;</span>)
<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>)
doc/pub/week38/html/week38.html
+8 -8
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@@ -859,10 +859,10 @@ clf <span style="color: #666666">=</span> LinearRegression(fit_intercept<span st
<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>)
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: </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>(MSE(y,ypredictOwn)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with intercept column from SKL: </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>(MSE(y,ypredictSKL)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with intercept column&quot;</span>)
<span style="color: #008000">print</span>(MSE(y,ypredictOwn))
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with intercept column from SKL&quot;</span>)
<span style="color: #008000">print</span>(MSE(y,ypredictSKL))
plt<span style="color: #666666">.</span>figure()
@@ -893,10 +893,10 @@ intercept <span style="color: #666666">=</span> np<span style="color: #666666">.
<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>)
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: </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>(MSE(y,ypredictOwn)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with 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>(MSE(y,ypredictSKL)
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with Manual intercept&quot;</span>)
<span style="color: #008000">print</span>(MSE(y,ypredictOwn))
<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;MSE with Sklearn intercept&quot;</span>)
<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>)
doc/pub/week38/ipynb/ipynb-week38-src.tar.gz
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doc/pub/week38/ipynb/week38.ipynb
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@@ -783,10 +783,10 @@
"print(f\"Sklearn fitted beta: {clf.coef_}\")\n",
"ypredictOwn = X @ beta\n",
"ypredictSKL = skl.predict(X)\n",
"print(f\"MSE with intercept column: {intercept}\")\n",
"print(MSE(y,ypredictOwn)\n",
"print(f\"MSE with intercept column from SKL: {intercept}\")\n",
"print(MSE(y,ypredictSKL)\n",
"print(f\"MSE with intercept column\")\n",
"print(MSE(y,ypredictOwn))\n",
"print(f\"MSE with intercept column from SKL\")\n",
"print(MSE(y,ypredictSKL))\n",
"\n",
"\n",
"plt.figure()\n",
@@ -817,10 +817,10 @@
"print(f\"Sklearn fitted beta (without intercept): {clf.coef_}\")\n",
"ypredictOwn = X @ beta\n",
"ypredictSKL = skl.predict(X)\n",
"print(f\"MSE with Manual intercept: {intercept}\")\n",
"print(MSE(y,ypredictOwn)\n",
"print(f\"MSE with Sklearn intercept: {clf.intercept_}\")\n",
"print(MSE(y,ypredictSKL)\n",
"print(f\"MSE with Manual intercept\")\n",
"print(MSE(y,ypredictOwn))\n",
"print(f\"MSE with Sklearn intercept\")\n",
"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",
doc/src/week38/week38.do.txt
+8 -8
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@@ -499,10 +499,10 @@ print(f"Fitted beta: {beta}")
print(f"Sklearn fitted beta: {clf.coef_}")
ypredictOwn = X @ beta
ypredictSKL = skl.predict(X)
print(f"MSE with intercept column: {intercept}")
print(MSE(y,ypredictOwn)
print(f"MSE with intercept column from SKL: {intercept}")
print(MSE(y,ypredictSKL)
print(f"MSE with intercept column")
print(MSE(y,ypredictOwn))
print(f"MSE with intercept column from SKL")
print(MSE(y,ypredictSKL))
plt.figure()
@@ -533,10 +533,10 @@ print(f"Sklearn intercept: {clf.intercept_}")
print(f"Sklearn fitted beta (without intercept): {clf.coef_}")
ypredictOwn = X @ beta
ypredictSKL = skl.predict(X)
print(f"MSE with Manual intercept: {intercept}")
print(MSE(y,ypredictOwn)
print(f"MSE with Sklearn intercept: {clf.intercept_}")
print(MSE(y,ypredictSKL)
print(f"MSE with Manual intercept")
print(MSE(y,ypredictOwn))
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)")