from algorithm import algorithm
import json
import ray

@ray.remote
class variance(algorithm):
    def __init__(self):
        self.config_dict_ = None
        self.config_ = None
        self.variance_number_ = 10
        self.present_number_ = 0
        self.window_1_ = []
        self.sum = 0
        self.average = 0
        self.variance = 0
        self.variance_sum = 0

    def set_config(self, config):
        self.config_ = config
        self.config_dict_ = json.loads(self.config_)
        self.variance_number_ = self.config_dict_["VARIANCE_NUMBER"]

    def config(self):
        return self.config_

    def eval(self, value):
        self.present_number_ = len(self.window_1_)
        if self.present_number_ < self.variance_number_:
            self.window_1_.append(value)
            self.sum = 0
            for i in self.window_1_:
                self.sum = self.sum + i
            self.average = self.sum / len(self.window_1_)
            self.variance_sum = 0
            for i in self.window_1_:
                self.variance_sum = self.variance_sum + (i-self.average)*(i-self.average)
            self.variance = self.variance_sum / len(self.window_1_)
            return self.variance
        else:
            self.sum = 0
            del self.window_1_[0]
            self.window_1_.append(value)
            for i in self.window_1_:
                self.sum = self.sum + i
            self.average = self.sum / len(self.window_1_)
            self.variance_sum = 0
            for i in self.window_1_:
                self.variance_sum = self.variance_sum + (i-self.average)*(i-self.average)
            self.variance = self.variance_sum / len(self.window_1_)
            return self.variance