Differences

This shows you the differences between two versions of the page.

@@ Line 1: / Line 1: @@
+=== Principal Components Analysis (PCA) ===
+<file python pca.py>
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.svm import SVC
+from sklearn import cross_validation
+from sklearn.decomposition import PCA
+from sklearn import preprocessing
+digits = datasets.load_digits()
+X = digits.data
+y = digits.target
+# if you want to standardize the data, uncomment the following lines
+#scaler = preprocessing.StandardScaler().fit(X)
+#X = scaler.transform(X)
+pca = PCA(n_components=10)
+X_reduced = pca.fit_transform(X)
+print (pca.explained_variance_ratio_)
+# a scatter-plot in the space of the principal components:
+plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y, cmap=plt.cm.Paired)
+# let's see if this feature representation is useful:
+X /= X.max()
+from sklearn.grid_search import GridSearchCV
+param_grid = [
+  {'C': [1, 10, 100], 'kernel': ['linear']},
+  {'C': [1, 10, 100], 'gamma': [0.01, 0.001, 0.0001], 'kernel': ['rbf']},
+ ]
+classifier = GridSearchCV(estimator=SVC(), param_grid=param_grid)
+cv = cross_validation.StratifiedKFold(y, 5, shuffle=True, random_state=0)
+# accuracy with all the features:
+print (np.mean(cross_validation.cross_val_score(classifier, X, y, cv=cv)))
+# accuracy with the PCA features:
+print (np.mean(cross_validation.cross_val_score(classifier, X_reduced, y, cv=cv)))
+</file>

CS545 fall 2016