def _svm_new(self, kernel_width, c, epsilon):
if self.x == None or self.y == None:
raise Exception("No training data loaded.")
x = RealFeatures(self.x)
y = Labels(self.y)
self.svm = GMNPSVM(c, GaussianKernel(x, x, kernel_width), y)
self.svm.set_epsilon(epsilon)
class Ai:
def __init__(self):
self.x = None
self.y = None
self.x_test = None
self.y_test = None
self.svm = None
def load_train_data(self, x_fname, y_fname):
Ai.__init__(self)
self.x = np.loadtxt(x_fname)
self.y = np.loadtxt(y_fname) - 1.0
self.x_test = self.x
self.y_test = self.y
def _svm_new(self, kernel_width, c, epsilon):
if self.x == None or self.y == None:
raise Exception("No training data loaded.")
x = RealFeatures(self.x)
y = Labels(self.y)
self.svm = GMNPSVM(c, GaussianKernel(x, x, kernel_width), y)
self.svm.set_epsilon(epsilon)
def write_svm(self):
gz_stream = gz.open(com.TRAIN_SVM_FNAME_GZ, 'wb', 9)
pkl.dump(self.svm, gz_stream)
gz_stream.close()
def read_svm(self):
gz_stream = gz.open(com.TRAIN_SVM_FNAME_GZ, 'rb')
self.svm = pkl.load(gz_stream)
gz_stream.close()
def enable_validation(self, train_frac):
x = self.x
y = self.y
idx = np.arange(len(y))
np.random.shuffle(idx)
train_idx=idx[:np.floor(train_frac*len(y))]
test_idx=idx[np.ceil(train_frac*len(y)):]
self.x = x[:,train_idx]
self.y = y[train_idx]
self.x_test = x[:,test_idx]
self.y_test = y[test_idx]
def train(self, kernel_width, c, epsilon):
self._svm_new(kernel_width, c, epsilon)
x = RealFeatures(self.x)
self.svm.io.enable_progress()
self.svm.train(x)
self.svm.io.disable_progress()
def load_classifier(self): self.read_svm()
def classify(self, matrix):
cl = self.svm.apply(
RealFeatures(
np.reshape(matrix, newshape=(com.FEATURE_DIM, 1),
order='F')
)
).get_label(0)
return int(cl + 1.0) % 10
def get_test_error(self):
self.svm.io.enable_progress()
l = self.svm.apply(RealFeatures(self.x_test)).get_labels()
self.svm.io.disable_progress()
return 1.0 - np.mean(l == self.y_test)
