'''
Created on 17 Nov 2015

@author: qinyi
'''
import time

from causaldmir.discovery.funtional_based.LearningHierarchicalStructure.indTest.HSICTestObject import HSICTestObject


class HSICPermutationTestObject(HSICTestObject):

    def __init__(self, num_samples, data_generator=None, kernelX=None, kernelY=None, kernelX_use_median=False,
                 kernelY_use_median=False, num_rfx=None, num_rfy=None, rff=False,
                 induce_set=False, num_inducex=None, num_inducey=None, num_shuffles=1000, unbiased=True):
        HSICTestObject.__init__(self, num_samples, data_generator=data_generator, kernelX=kernelX, kernelY=kernelY,
                                kernelX_use_median=kernelX_use_median, kernelY_use_median=kernelY_use_median,
                                num_rfx=num_rfx, num_rfy=num_rfy, rff=rff, induce_set=induce_set,
                                num_inducex=num_inducex, num_inducey=num_inducey)
        self.num_shuffles = num_shuffles
        self.unbiased = unbiased

    def compute_pvalue_with_time_tracking(self, data_x=None, data_y=None):
        if data_x is None and data_y is None:
            if not self.streaming and not self.freeze_data:
                start = time.perf_counter()
                self.generate_data()
                data_generating_time = time.perf_counter() - start
                data_x = self.data_x
                data_y = self.data_y
            else:
                data_generating_time = 0.
        else:
            data_generating_time = 0.

        print('Permutation data generating time passed: ', data_generating_time)

        hsic_statistic, null_samples, _, _, _, _, _ = self.HSICmethod(unbiased=self.unbiased,
                                                                      num_shuffles=self.num_shuffles,
                                                                      data_x=data_x, data_y=data_y)
        pvalue = (1 + sum(null_samples > hsic_statistic)) / float(1 + self.num_shuffles)

        return pvalue, data_generating_time
