def fit(self, X, Y):

        # copy since this will contains the residuals (deflated) matrices
        check_consistent_length(X, Y)
        X = check_array(X, dtype=np.float64, copy=self.copy)
        Y = check_array(Y, dtype=np.float64, copy=self.copy, ensure_2d=False)
        if Y.ndim == 1:
            Y = Y.reshape(-1, 1)

        n = X.shape[0]
        p = X.shape[1]
        q = Y.shape[1]

        if self.n_components < 1 or self.n_components > p:
            raise ValueError('Invalid number of components: %d' %
                             self.n_components)
        if self.algorithm not in ("svd", "nipals"):
            raise ValueError("Got algorithm %s when only 'svd' "
                             "and 'nipals' are known" % self.algorithm)
        if self.algorithm == "svd" and self.mode == "B":
            raise ValueError('Incompatible configuration: mode B is not '
                             'implemented with svd algorithm')
        if self.deflation_mode not in ["canonical", "regression"]:
            raise ValueError('The deflation mode is unknown')
        # Scale (in place)
        X, Y, self.x_mean_, self.y_mean_, self.x_std_, self.y_std_ = (
            _center_scale_xy(X, Y, self.scale))
        # Residuals (deflated) matrices
        Xk = X
        Yk = Y
        # Results matrices
        self.x_scores_ = np.zeros((n, self.n_components))
        self.y_scores_ = np.zeros((n, self.n_components))
        self.x_weights_ = np.zeros((p, self.n_components))
        self.y_weights_ = np.zeros((q, self.n_components))
        self.x_loadings_ = np.zeros((p, self.n_components))
        self.y_loadings_ = np.zeros((q, self.n_components))
        self.n_iter_ = []

        # NIPALS algo: outer loop, over components
        for k in range(self.n_components):
            if np.all(np.dot(Yk.T, Yk) < np.finfo(np.double).eps):
                # Yk constant
                warnings.warn('Y residual constant at iteration %s' % k)
                break
            # 1) weights estimation (inner loop)
            # -----------------------------------
            if self.algorithm == "nipals":
                x_weights, y_weights, n_iter_ = \
                    _nipals_twoblocks_inner_loop(
                        X=Xk, Y=Yk, mode=self.mode, max_iter=self.max_iter,
                        tol=self.tol, norm_y_weights=self.norm_y_weights)
                self.n_iter_.append(n_iter_)
            elif self.algorithm == "svd":
                x_weights, y_weights = _svd_cross_product(X=Xk, Y=Yk)
            # Forces sign stability of x_weights and y_weights
            # Sign undeterminacy issue from svd if algorithm == "svd"
            # and from platform dependent computation if algorithm == 'nipals'
            x_weights, y_weights = svd_flip(x_weights, y_weights.T)
            y_weights = y_weights.T
            # compute scores
            x_scores = np.dot(Xk, x_weights)
            if self.norm_y_weights:
                y_ss = 1
            else:
                y_ss = np.dot(y_weights.T, y_weights)
            y_scores = np.dot(Yk, y_weights) / y_ss
            # test for null variance
            if np.dot(x_scores.T, x_scores) < np.finfo(np.double).eps:
                warnings.warn('X scores are null at iteration %s' % k)
                break
            # 2) Deflation (in place)
            # ----------------------
            # Possible memory footprint reduction may done here: in order to
            # avoid the allocation of a data chunk for the rank-one
            # approximations matrix which is then subtracted to Xk, we suggest
            # to perform a column-wise deflation.
            #
            # - regress Xk's on x_score
            x_loadings = np.dot(Xk.T, x_scores) / np.dot(x_scores.T, x_scores)
            # - subtract rank-one approximations to obtain remainder matrix
            Xk -= np.dot(x_scores, x_loadings.T)
            if self.deflation_mode == "canonical":
                # - regress Yk's on y_score, then subtract rank-one approx.
                y_loadings = (np.dot(Yk.T, y_scores)
                              / np.dot(y_scores.T, y_scores))
                Yk -= np.dot(y_scores, y_loadings.T)
            if self.deflation_mode == "regression":
                # - regress Yk's on x_score, then subtract rank-one approx.
                y_loadings = (np.dot(Yk.T, x_scores)
                              / np.dot(x_scores.T, x_scores))
                Yk -= np.dot(x_scores, y_loadings.T)
            # 3) Store weights, scores and loadings # Notation:
            self.x_scores_[:, k] = x_scores.ravel()  # T
            self.y_scores_[:, k] = y_scores.ravel()  # U
            self.x_weights_[:, k] = x_weights.ravel()  # W
            self.y_weights_[:, k] = y_weights.ravel()  # C
            self.x_loadings_[:, k] = x_loadings.ravel()  # P
            self.y_loadings_[:, k] = y_loadings.ravel()  # Q
        # Such that: X = TP' + Err and Y = UQ' + Err

        # 4) rotations from input space to transformed space (scores)
        # T = X W(P'W)^-1 = XW* (W* : p x k matrix)
        # U = Y C(Q'C)^-1 = YC* (W* : q x k matrix)
        self.x_rotations_ = np.dot(
            self.x_weights_,
            linalg.pinv2(np.dot(self.x_loadings_.T, self.x_weights_),
                         **pinv2_args))
        if Y.shape[1] > 1:
            self.y_rotations_ = np.dot(
                self.y_weights_,
                linalg.pinv2(np.dot(self.y_loadings_.T, self.y_weights_),
                             **pinv2_args))
        else:
            self.y_rotations_ = np.ones(1)

        if True or self.deflation_mode == "regression":
            # FIXME what's with the if?
            # Estimate regression coefficient
            # Regress Y on T
            # Y = TQ' + Err,
            # Then express in function of X
            # Y = X W(P'W)^-1Q' + Err = XB + Err
            # => B = W*Q' (p x q)
            self.coef_ = np.dot(self.x_rotations_, self.y_loadings_.T)
            self.coef_ = (1. / self.x_std_.reshape((p, 1)) * self.coef_ *
                          self.y_std_)
        return self
This method should be implemented by all subclasses of
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/cross_decomposition/pls_.py/_PLS.fit--crossover with--dataset/ETHPy150Open dimagi/commcare-hq/corehq/apps/sms/mixin.py/CommCareMobileContactMixin.get_email