import numpy as np
import scipy as sp
import sklearn 
from sklearn import linear_model
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score

class SCL(object):
    class of structural correspondence learning 
    def __init__(self, l2=1.0, num_pivots=10, base_classifer=LinearSVC()):
        self.l2 = l2
        self.num_pivots = num_pivots
        self.W = 0
        self.base_classifer = base_classifer
        # self.train_data_dim = None

    def fit(self, Xs, Xt):
        find pivot features and transfer the Xs and Xt
        Param Xs: source data
        Param Xt: target data
        output Xs_new: new source data features
        output Xt_new: new target data features
        output W: transform matrix
        _, ds = Xs.shape
        _, dt = Xt.shape
        assert ds == dt
        X = np.concatenate((Xs, Xt), axis=0)
        ix = np.argsort(np.sum(X, axis=0))
        ix = ix[::-1][:self.num_pivots]
        pivots = (X[:, ix]>0).astype('float')
        p = np.zeros((ds, self.num_pivots))
        # train for the classifers 
        for i in range(self.num_pivots):
            clf = linear_model.SGDClassifier(loss="modified_huber", alpha=self.l2)
  , pivots[:, i])
            p[:, i] = clf.coef_
        _, W = np.linalg.eig(np.cov(p))
        W = W[:, :self.num_pivots].astype('float')
        self.W = W
        Xs_new = np.concatenate((, W), Xs), axis=1)
        Xt_new = np.concatenate((, W), Xt), axis=1)

        return Xs_new, Xt_new, W

    def transform(self, X):
        transform the origianl data by add new features
        Param X: original data
        output x_new: X with new features
        X_new = np.concatenate((, self.W),X), axis=1)
        return X_new
    def fit_predict(self, Xs, Xt, X_test, Ys, Y_test):, Xt)
        Xs = self.transform(Xs), Ys)
        X_test = self.transform(X_test)
        y_pred = self.base_classifer.predict(X_test)
        acc = accuracy_score(Y_test, y_pred)
        return acc