more irritating typos

doc/src/week38/week38.do.txt

@@ -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()