From a4c4204cda55f4054c28842fdb9bb9846a886434 Mon Sep 17 00:00:00 2001 From: mhjensen Date: Wed, 16 Oct 2019 10:47:37 +0200 Subject: [PATCH] editing code, needed to save --- doc/src/DimRed/cancerownlogreg.py | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/doc/src/DimRed/cancerownlogreg.py b/doc/src/DimRed/cancerownlogreg.py index 45f03f36a..a87641450 100644 --- a/doc/src/DimRed/cancerownlogreg.py +++ b/doc/src/DimRed/cancerownlogreg.py @@ -23,18 +23,22 @@ fig.tight_layout() plt.show() X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0) -print(X_train.shape) -print(X_test.shape) logreg = LogisticRegression() logreg.fit(X_train, y_train) print("Test set accuracy from Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test))) -from sklearn.preprocessing import MinMaxScaler, StandardScaler -scaler = StandardScaler() -scaler.fit(X_train) -X_train_scaled = scaler.transform(X_train) -X_test_scaled = scaler.transform(X_test) -logreg.fit(X_train_scaled, y_train) -print("Test set accuracy scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test))) + +beta = np.random.randn(2,1) + +eta = 0.1 +Niterations = 100 + +for iter in range(Niterations): + gradients = 2.0/m*xb.T @ (xb @ (beta)-y)+2*lmbda*beta + beta -= eta*gradients + +print(beta) +ypredict = xb @ beta +ypredict2 = xb @ beta_linreg