def main():
input_dir = sys.argv[1]
num_samples_per_site = int(sys.argv[2])
target_sites = get_target_sites(input_dir)
labels = dict(map(lambda (i, s): (s, i), enumerate(target_sites)))
X, Y = load_feature_vectors(input_dir, num_samples_per_site, labels)
X, Y, testX, testY = select_test_set(X, Y, (num_samples_per_site / 2) * len(target_sites))
# X, testX = scale(X, testX)
X = [[0.0, 0.0], [0.01, 0.01], [1.0, 1.0], [1.1, 1.1], [2.0, 2.0], [2.1, 2.1], [2.05, 2.05]]#, [2.05, 2.05], [2.05, 2.07], [2.01, 2.01]]
Y=[0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 1.0]#, 1.0, 2.0, 2.0]
labels = {0: 0, 1: 1, 2: 2}
thetas, bs, slacks = SVM_fit(X, Y, len(labels), 5.0)
print "thetas"
print thetas[0]
print thetas[1]
print thetas[2]
print "bs"
print bs
print "slacks"
print slacks
print "Should be class 0:"
print SVM_classify([0.05, 0.05], thetas, bs)
print "Should be class 1:"
print SVM_classify([1.05, 1.05], thetas, bs)
print "Should be class 2:"
print SVM_classify([1.98, 1.98], thetas, bs)
def main():
cmd = sys.argv[3]
input_dir = sys.argv[1]
num_samples_per_site = int(sys.argv[2])
target_sites = get_target_sites(input_dir)
labels = dict(map(lambda (i, s): (s, i), enumerate(target_sites)))
X, Y = load_feature_vectors(input_dir, num_samples_per_site, labels)
X, Y, testX, testY = select_test_set(X, Y,
(num_samples_per_site / 2) * len(target_sites))
Y = map(lambda v: v*1.0, Y)
testY = map(lambda v: v*1.0, testY)
pca = PCA(n_components = 50)
print "Fitting X"
pca.fit(X)
print "Transforming X and testX"
Xnew = pca.transform(X)
testXnew = pca.transform(testX)
del X
del testX
if cmd == "monb":
multiclass_on_binary_svms(Xnew, Y, testXnew, testY)
elif cmd == "multiclass":
multiclass_svm(Xnew, testXnew, Y, testY, labels)
elif cmd == "anomdet":
anomaly_detection(labels, Xnew, Y, testXnew, testY)
def anom_det_test(labels, X, Y, v, ntest):
X, Y, testX, testY = select_test_set(X, Y, ntest)
_anom_det_test(labels, X, Y, testX, testY, v)
def monb_test(X, Y, c, onevone, ntest):
X, Y, testX, testY = select_test_set(X, Y, ntest)
Y = map(lambda v: v*1.0, Y)
classify(X, Y, testX, testY, onevone, c)
def multiclass_svm_test(X, Y, labels, c, ntests):
X, Y, testX, testY = select_test_set(X, Y, ntests)
multiclass_svm(X, Y, testX, testY, labels, c)
