#standard imports from numpy, scikit-learn and matplotlib import numpy as np from time import time import matplotlib.pyplot as plt from sklearn.metrics import accuracy_score from sklearn import svm, pipeline from sklearn.svm import LinearSVC from sklearn.kernel_approximation import (RBFSampler, Nystroem) from sklearn.preprocessing import StandardScaler # implementations of the proposed feature maps import FeatureMaps as maps # data reading module import DataReader as DR #get the dataset X_train, X_test, y_train, y_test = DR.Magic() #scale the dataset scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) #exact rbf performance start = time() rbf_clf = svm.SVC(C=1, kernel='rbf', gamma=0.2).fit(X_train, y_train) rbf_time = (time() - start) rbf_score = accuracy_score(y_test, rbf_clf.predict(X_test)) #linear performance start = time() linear_clf = LinearSVC(C=1, dual=False).fit(X_train, y_train)
