import numpy as np
from sklearn.base import BaseEstimator
from sklearn.base import TransformerMixin
from sklearn.utils import check_array
class CycleTransformer(BaseEstimator, TransformerMixin):
def fit(self, X, y=None, **fit_params):
# scikit-learn checks
X = check_array(X)
self.maximums_ = np.max(X, axis=0)
return self
def transform(self, X, y=None):
# scikit-learn checks
X = check_array(X)
if X.shape[1] != len(self.maximums_):
raise ValueError("X has different shape than during fitting. "
"Expected %d, got %d." % (len(self.maximums_), X.shape[1]))
return np.vstack((
np.array([
np.cos(2 * np.pi * x / (maximum + 1))
for x, maximum in zip(X.T, self.maximums_)
]),
np.array([
np.sin(2 * np.pi * x / (maximum + 1))
for x, maximum in zip(X.T, self.maximums_)
])
)).T
