from readFile import readDataSet from sklearn.svm import SVR from sklearn.externals import joblib from sklearn.model_selection import GridSearchCV import numpy as np data, nrows, ncols = readDataSet("YearPredictionMSD20.txt") X = data[:, 1:91] y = data[:, 0] # clf = SVR(C=1.0, epsilon=0.2,max_iter=100000000,degree = 3, coef0 = 0 ,kernel="poly",verbose=True) params = { 'kernel': ['rbf', 'linear', 'poly'], 'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 50, 100], 'epsilon': [0.2, 1, 0.001, 0.01, 10] } clf = GridSearchCV(SVR(degree=3, max_iter=1000, verbose=True), params, cv=5, verbose=True) clf.fit(X, y) y_pred = clf.predict(X) print y_pred, y print(np.sum((y_pred - y)**2) / len(X)) print clf.score(X, y) print clf.best_estimator_ print clf.best_score_ print clf.best_params_ print clf.cv_results_ # print clf.support_
from readFile import readDataSet from sklearn.linear_model import Ridge from sklearn.externals import joblib from sklearn.model_selection import GridSearchCV import numpy as np from sklearn.preprocessing import StandardScaler data, nrows, ncols = readDataSet("YearPredictionMSD100.txt") X = data[:, 1:91] y = data[:, 0] X = StandardScaler().fit_transform(X) clf = Ridge(alpha=100, max_iter=-1, solver="lsqr") params = {'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000]} # clf = GridSearchCV(Ridge(max_iter=-1), params, cv = 5,verbose=True) clf.fit(X, y) # print clf.best_estimator_ # print clf.best_score_ # print clf.best_params_ # print clf.cv_results_ # print clf.cv_results_['mean_test_score'] y_pred = clf.predict(X) print y_pred, y print(np.sum((y_pred - y)**2) / len(X)) print clf.score(X, y) data, nrows, ncols = readDataSet("YearPredictionMSDTest10.txt") X = data[:, 1:91] y = data[:, 0]