'Compute percentiles for dense matrices.
Parameters
X : ndarray, shape (n_samples, n_features)
The data used to scale along the features axis.'

def _dense_fit(self, X, random_state):

    if self.ignore_implicit_zeros:
        warnings.warn("'ignore_implicit_zeros'    takes    effect    only    with    sparse    matrix.    This    parameter    has    no    effect.")
    (n_samples, n_features) = X.shape
    references = (self.references_ * 100).tolist()
    self.quantiles_ = []
    for col in X.T:
        if (self.subsample < n_samples):
            subsample_idx = random_state.choice(n_samples, size=self.subsample, replace=False)
            col = col.take(subsample_idx, mode='clip')
        self.quantiles_.append(np.percentile(col, references))
    self.quantiles_ = np.transpose(self.quantiles_)
