import numpy as np


class DTLearner(object):

    LEAF = -1
    NA = -1

    def __init__(self, leaf_size = 1, verbose = False):
        self.leaf_size = leaf_size
        self.verbose = verbose

    def author(self):
        return 'felixm' # replace tb34 with your Georgia Tech username

    def create_node(self, factor: int, split: int, left: int, right: int):
        return np.array((factor, split, left, right))


    def build_tree(self, xs, y):
        assert(xs.shape[0] == y.shape[0])
        assert(xs.shape[0] > 0) # If this is 0 something went wrong.

        if xs.shape[0] == 1:
            return  self.create_node(self.LEAF, y[0], self.NA, self.NA)

        if np.all(y[0] == y):
            return self.create_node(self.LEAV, y[0], self.NA, self.NA)

        # XXX: continue here
        y = np.array([y])
        correlations = np.corrcoef(xs, y, rowvar=True)
        print(f"{correlations=}")

        return 0

    def addEvidence(self, data_x, data_y):
        """
        @summary: Add training data to learner
        @param dataX: X values of data to add
        @param dataY: the Y training values
        """
        if self.verbose:
            print(data_x)
            print(data_y)
        self.tree = self.build_tree(data_x, data_y)



    def query(self,points):
        """
        @summary: Estimate a set of test points given the model we built.
        @param points: should be a numpy array with each row corresponding to a specific query.
        @returns the estimated values according to the saved model.
        """
        return
        # return (self.model_coefs[:-1] * points).sum(axis = 1) + self.model_coefs[-1]

if __name__=="__main__":
    print("the secret clue is 'zzyzx'")