def main():
pk=80
for pk in [80, 200, 500, 1024]:
proj_test, labels = load_pca_proj(K=pk)
shuffle_in_unison(proj_test, labels)
for k in [1,2,3,4,5,6,7,8,9,15,20,25,30]:
knn = KNeighborsClassifier(n_neighbors=k)
scores = cross_validation.cross_val_score(knn, proj_test, labels, cv=10)
print "K: " + str(k)
print "PK: " + str(pk)
print scores
print np.mean(scores)
print np.var(scores)
def main():
N_TREE = 1001
k = 200
rfc = RandomForestClassifier(n_estimators=N_TREE, criterion="entropy", max_features="auto")
RandomForestClassifier
proj_test, labels = load_pca_proj(K=k)
shuffle_in_unison(proj_test, labels)
scores = cross_validation.cross_val_score(rfc, proj_test, labels, cv=10)
pt = load_pca_test(K=k)
rfc.fit(proj_test, labels)
pred = rfc.predict(pt)
write_results(pred, "../rfc_res.csv")
print scores
print np.mean(scores)
print np.var(scores)
def main():
N_TREE = 1001
k = 200
rfc = RandomForestClassifier(n_estimators=N_TREE, criterion='entropy', max_features="auto")
RandomForestClassifier
proj_test, labels = load_pca_proj(K=k)
shuffle_in_unison(proj_test, labels)
scores = cross_validation.cross_val_score(rfc, proj_test, labels, cv=10)
pt = load_pca_test(K=k)
rfc.fit(proj_test, labels)
pred = rfc.predict(pt)
write_results(pred, '../rfc_res.csv')
print scores
print np.mean(scores)
print np.var(scores)
def main():
pk = 80
for pk in [80, 200, 500, 1024]:
proj_test, labels = load_pca_proj(K=pk)
shuffle_in_unison(proj_test, labels)
for k in [1, 2, 3, 4, 5, 6, 7, 8, 9, 15, 20, 25, 30]:
knn = KNeighborsClassifier(n_neighbors=k)
scores = cross_validation.cross_val_score(knn,
proj_test,
labels,
cv=10)
print "K: " + str(k)
print "PK: " + str(pk)
print scores
print np.mean(scores)
print np.var(scores)
def main():
k=500
#images, labels = load_labeled_training(flatten=True)
#images = standardize(images)
#shuffle_in_unison(images, labels)
#for k in [100,500,1024]:
#proj_test, labels = load_pca_proj(K=k)
#shuffle_in_unison(proj_test, labels)
#for ker in ['linear', 'sigmoid', 'rbf']:
#svc = SVC(kernel=ker)
#scores = cross_validation.cross_val_score(svc, proj_test, labels, cv=10)
#print "Kernel: " + ker
#print "K: " + str(k)
#print scores
#print np.mean(scores)
#print np.var(scores)
proj_test, labels = load_pca_proj(K=k)
shuffle_in_unison(proj_test, labels)
svc = SVC()
scores = cross_validation.cross_val_score(svc, proj_test, labels, cv=10)
pt = load_pca_hidden(K=k)
svc.fit(proj_test, labels)
pred = svc.predict(pt)
write_results(pred, '../svm_res.csv')
def main():
k = 500
#images, labels = load_labeled_training(flatten=True)
#images = standardize(images)
#shuffle_in_unison(images, labels)
#for k in [100,500,1024]:
#proj_test, labels = load_pca_proj(K=k)
#shuffle_in_unison(proj_test, labels)
#for ker in ['linear', 'sigmoid', 'rbf']:
#svc = SVC(kernel=ker)
#scores = cross_validation.cross_val_score(svc, proj_test, labels, cv=10)
#print "Kernel: " + ker
#print "K: " + str(k)
#print scores
#print np.mean(scores)
#print np.var(scores)
proj_test, labels = load_pca_proj(K=k)
shuffle_in_unison(proj_test, labels)
svc = SVC()
scores = cross_validation.cross_val_score(svc, proj_test, labels, cv=10)
pt = load_pca_hidden(K=k)
svc.fit(proj_test, labels)
pred = svc.predict(pt)
write_results(pred, '../svm_res.csv')