From d69f43cbaf9ec288a59cd4915acfd7992028d221 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Thu, 23 Sep 2021 09:09:42 +0200 Subject: [PATCH] more irritating typos --- doc/pub/week38/html/._week38-bs014.html | 14 +++++++------- doc/pub/week38/html/week38-reveal.html | 14 +++++++------- doc/pub/week38/html/week38-solarized.html | 14 +++++++------- doc/pub/week38/html/week38.html | 14 +++++++------- doc/pub/week38/ipynb/ipynb-week38-src.tar.gz | Bin 193 -> 192 bytes doc/pub/week38/ipynb/week38.ipynb | 14 +++++++------- doc/src/week38/week38.do.txt | 14 +++++++------- 7 files changed, 42 insertions(+), 42 deletions(-) diff --git a/doc/pub/week38/html/._week38-bs014.html b/doc/pub/week38/html/._week38-bs014.html index 8290e2bb1..4c330102a 100644 --- a/doc/pub/week38/html/._week38-bs014.html +++ b/doc/pub/week38/html/._week38-bs014.html @@ -460,11 +460,11 @@ X = np.z 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") @@ -476,7 +476,7 @@ ypredictSKL = skl.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 @@ -486,7 +486,7 @@ X = np.z 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) @@ -497,8 +497,8 @@ intercept = np. 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") @@ -507,7 +507,7 @@ ypredictSKL = sklprint(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() diff --git a/doc/pub/week38/html/week38-reveal.html b/doc/pub/week38/html/week38-reveal.html index 22973f249..9e09c6fa9 100644 --- a/doc/pub/week38/html/week38-reveal.html +++ b/doc/pub/week38/html/week38-reveal.html @@ -713,11 +713,11 @@ X = np.zeros((len(x), 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") @@ -729,7 +729,7 @@ ypredictSKL = skl.predict(X) 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 @@ -739,7 +739,7 @@ X = np.zeros((len(x), degree - 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) @@ -750,8 +750,8 @@ 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") @@ -760,7 +760,7 @@ ypredictSKL = skl.predict(X) 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() diff --git a/doc/pub/week38/html/week38-solarized.html b/doc/pub/week38/html/week38-solarized.html index da89caf98..d4666dff6 100644 --- a/doc/pub/week38/html/week38-solarized.html +++ b/doc/pub/week38/html/week38-solarized.html @@ -847,11 +847,11 @@ X = np.zeros((len(x), 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") @@ -863,7 +863,7 @@ ypredictSKL = skl.predict(X) 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 @@ -873,7 +873,7 @@ X = np.zeros((len(x), degree - 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) @@ -884,8 +884,8 @@ 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") @@ -894,7 +894,7 @@ ypredictSKL = skl.predict(X) 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() diff --git a/doc/pub/week38/html/week38.html b/doc/pub/week38/html/week38.html index d467ae5e5..ec13a9371 100644 --- a/doc/pub/week38/html/week38.html +++ b/doc/pub/week38/html/week38.html @@ -852,11 +852,11 @@ X = np.z 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") @@ -868,7 +868,7 @@ ypredictSKL = skl.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 @@ -878,7 +878,7 @@ X = np.z 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) @@ -889,8 +889,8 @@ intercept = np. 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") @@ -899,7 +899,7 @@ ypredictSKL = sklprint(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() diff --git a/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz b/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz index 1deffa1e8712ca255dbeddde43619c84e77dd901..a57f336d8c0a990810958deaa8a76dd624df173c 100644 GIT binary patch literal 192 zcmV;x06+g9iwFQGC`@4h1MSaC4uUWc24L2lVopG&P{6H;EL@lv;{{46Qjr$h66N;t zNOYxfLySqk&7Wx}lbJ)d-t4l#-mSM7L`*1!G1DZT62rNk5E28SB#eevq$wbY;t)N+ zjAeRQrK!$b8ZO`U4Q*xlVa|LCJo8T+D`8-}?`@?dNM(7hR1G)I>u42-wwE~+ uiktBZG{1I2b6~OuRusYtCE3MqwK{3mn85$_F^=On&etAuHia9}