def test_depth_building(self):
self.setUp(m=10)
X = self.x.copy()
X -= X.mean(axis=0)
X /= X.std(axis=0)
kernel = SP.dot(X, X.T)
train = SP.where(self.train)[0]
test = SP.where(~self.train)[0]
model = MF(fit_optimal_depth=True, max_depth=3,
kernel=kernel[SP.ix_(train, train)])
model.fit(self.x[self.train], self.y[self.train],
fit_optimal_depth=True)
prediction_1 = model.predict(X[test], k=kernel[test, train],
depth=model.opt_depth)
# Grow to end
model.further()
# Prediction again
prediction_2 = model.predict(X[test], k=kernel[test, train],
depth=model.opt_depth)
self.assertEqual((prediction_1 - prediction_2).sum(), 0.0)
def test_depth_building(self):
self.setUp(m=10)
X = self.x.copy()
X -= X.mean(axis=0)
X /= X.std(axis=0)
kernel = SP.dot(X, X.T)
train = SP.where(self.train)[0]
test = SP.where(~self.train)[0]
model = MF(fit_optimal_depth=True,
max_depth=3,
kernel=kernel[SP.ix_(train, train)])
model.fit(self.x[self.train],
self.y[self.train],
fit_optimal_depth=True)
prediction_1 = model.predict(X[test],
k=kernel[test, train],
depth=model.opt_depth)
# Grow to end
model.further()
# Prediction again
prediction_2 = model.predict(X[test],
k=kernel[test, train],
depth=model.opt_depth)
self.assertEqual((prediction_1 - prediction_2).sum(), 0.0)
