# digits = load_digits()
# X, y = digits.data, digits.target
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
# knn = KNN(n_neighbors=5)
# pred = knn.fit(X_train, y_train).predict(X_test)
# print '{}/{}'.format( (pred == y_test).sum(), len(y_test) )
print "\nTest 3, LFW"
from sklearn.datasets import fetch_lfw_people
from sklearn.decomposition import PCA
lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
pca = PCA(n_components=10)
X = pca.fit_transform(lfw_people.data)
y = lfw_people.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
knn = KNN(n_neighbors=5)
pred = knn.fit(X_train, y_train).predict(X_test)
print '{}/{}'.format( (pred == y_test).sum(), len(y_test) )
nca = NCA()
X_train = nca.fit(X_train, y_train).transform(X_train)
X_test = nca.transform(X_test)
knn = KNN(n_neighbors=5)
pred = knn.fit(X_train, y_train).predict(X_test)
print '{}/{}'.format( (pred == y_test).sum(), len(y_test) )
# import cProfile
# cProfile.run('knn.predict(X_test)')
# print mcml.A
# print mcml.transform(X)
import sys; sys.path.append('/home/shaofan/Projects')
from FastML import KNN
print "\nTest 3, LFW"
from sklearn.datasets import fetch_lfw_people
from sklearn.decomposition import PCA
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import normalize
lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
pca = PCA(n_components=10)
X = pca.fit_transform(lfw_people.data)
X = normalize(X)
y = lfw_people.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
knn = KNN(n_neighbors=2)
pred = knn.fit(X_train, y_train).predict(X_test)
print '{}/{}'.format( (pred == y_test).sum(), len(y_test) )
knn = KNN(n_neighbors=2)
mcml = MCML()
for _ in range(100):
X_train = mcml.fit(X_train, y_train, max_iter=5, lr=1e-6).transform(X_train)
X_test = mcml.transform(X_test)
pred = knn.fit(X_train, y_train).predict(X_test)
print '{}/{}'.format( (pred == y_test).sum(), len(y_test) )
import sys
sys.path.append('/home/shaofan/Projects')
from FastML import KNN
print "\nTest 3, LFW"
from sklearn.datasets import fetch_lfw_people
from sklearn.decomposition import PCA
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import normalize
lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
pca = PCA(n_components=100)
X = pca.fit_transform(lfw_people.data)
X = normalize(X)
y = lfw_people.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=42)
knn = KNN(n_neighbors=4)
pred = knn.fit(X_train, y_train).predict(X_test)
print '{}/{}'.format((pred == y_test).sum(), len(y_test))
nca = NCA()
X_train = nca.fit(X_train, y_train, max_iter=10,
lr=5e-3).transform(X_train)
X_test = nca.transform(X_test)
knn = KNN(n_neighbors=4)
pred = knn.fit(X_train, y_train).predict(X_test)
print '{}/{}'.format((pred == y_test).sum(), len(y_test))
