def test_isotonic_dtype():
y = [2, 1, 4, 3, 5]
weights = np.array([.9, .9, .9, .9, .9], dtype=np.float64)
reg = IsotonicRegression()
for dtype in (np.int32, np.int64, np.float32, np.float64):
for sample_weight in (None, weights.astype(np.float32), weights):
y_np = np.array(y, dtype=dtype)
expected_dtype = \
check_array(y_np, dtype=[np.float64, np.float32],
ensure_2d=False).dtype
res = isotonic_regression(y_np, sample_weight=sample_weight)
assert res.dtype == expected_dtype
X = np.arange(len(y)).astype(dtype)
reg.fit(X, y_np, sample_weight=sample_weight)
res = reg.predict(X)
assert res.dtype == expected_dtype
def predict(self, X):
X = check_array(X)
self.key = 1000
return np.ones(X.shape[0])
def transform(self, X):
X = check_array(X)
if X.shape[1] != self.X_shape_[1]:
raise ValueError('Bad number of features')
return sp.csr_matrix(X)
def fit(self, X, y=None):
self.X_shape_ = check_array(X).shape
return self
def predict(self, X):
# return 1 if X has more than one element else return 0
X = check_array(X)
if X.shape[0] > 1:
return np.ones(X.shape[0])
return np.zeros(X.shape[0])
def transform(self, X):
X = check_array(X)
return X
def fit(self, X, y=None):
X = check_array(X)
return self
def predict(self, X):
X = check_array(X)
return np.ones(X.shape[0])
def predict(self, X):
check_is_fitted(self)
X = check_array(X)
return np.ones(X.shape[0])