diff --git a/doc/pub/week36/html/._week36-bs000.html b/doc/pub/week36/html/._week36-bs000.html index e1557c0ca..50b40fae8 100644 --- a/doc/pub/week36/html/._week36-bs000.html +++ b/doc/pub/week36/html/._week36-bs000.html @@ -8,8 +8,8 @@ doconce format html week36.do.txt --html_style=bootstrap --pygments_html_style=d - -
The Scikit-Learn site https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section has a good discussion of different ways of preprocessing data.
+Suppose as an example that you you have an input variable given by the heights of different persons. Human height might be measured in inches or meters or @@ -541,7 +543,7 @@ beta = fit_beta(X = np.mean(y_offset - X_offset @ beta) print(f"Manual intercept: {intercept}") -print(f"Fitted beta (wiothout intercept): {beta}") +print(f"Fitted beta (without intercept): {beta}") print(f"Sklearn intercept: {skl.intercept_}") print(f"Sklearn fitted beta (without intercept): {skl.coef_}") ypredictOwn = X @ beta diff --git a/doc/pub/week36/html/._week36-bs003.html b/doc/pub/week36/html/._week36-bs003.html index caad5a5d0..1ec9f00f0 100644 --- a/doc/pub/week36/html/._week36-bs003.html +++ b/doc/pub/week36/html/._week36-bs003.html @@ -8,8 +8,8 @@ doconce format html week36.do.txt --html_style=bootstrap --pygments_html_style=d - -
The Scikit-Learn site https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section has a good discussion of different ways of preprocessing data.
+Suppose as an example that you you have an input variable given by the heights of different persons. Human height might be measured in inches or meters or @@ -536,7 +538,7 @@ beta = fit_beta(X - X_offset, y - y_offset) intercept = np.mean(y_offset - X_offset @ beta) print(f"Manual intercept: {intercept}") -print(f"Fitted beta (wiothout intercept): {beta}") +print(f"Fitted beta (without intercept): {beta}") print(f"Sklearn intercept: {skl.intercept_}") print(f"Sklearn fitted beta (without intercept): {skl.coef_}") ypredictOwn = X @ beta diff --git a/doc/pub/week36/html/week36-solarized.html b/doc/pub/week36/html/week36-solarized.html index 61248b081..6d6af5071 100644 --- a/doc/pub/week36/html/week36-solarized.html +++ b/doc/pub/week36/html/week36-solarized.html @@ -8,8 +8,8 @@ doconce format html week36.do.txt --pygments_html_style=perldoc --html_style=sol - -
The Scikit-Learn site https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section has a good discussion of different ways of preprocessing data.
+Suppose as an example that you you have an input variable given by the heights of different persons. Human height might be measured in inches or meters or @@ -536,7 +538,7 @@ beta = fit_beta(X - X_offset, y - y_offset) intercept = np.mean(y_offset - X_offset @ beta) print(f"Manual intercept: {intercept}") -print(f"Fitted beta (wiothout intercept): {beta}") +print(f"Fitted beta (without intercept): {beta}") print(f"Sklearn intercept: {skl.intercept_}") print(f"Sklearn fitted beta (without intercept): {skl.coef_}") ypredictOwn = X @ beta diff --git a/doc/pub/week36/html/week36.html b/doc/pub/week36/html/week36.html index 7a8cc45e0..382b013e0 100644 --- a/doc/pub/week36/html/week36.html +++ b/doc/pub/week36/html/week36.html @@ -8,8 +8,8 @@ doconce format html week36.do.txt --pygments_html_style=default --html_style=blo - -