#!/usr/bin/env python
# -*- coding=utf-8 -*-
"""
Tencent is pleased to support the open source community by making Metis available.
Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the BSD 3-Clause License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
https://opensource.org/licenses/BSD-3-Clause
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
"""

import numpy as np


class Ewma(object):
    """
    In statistical quality control, the EWMA chart (or exponentially weighted moving average chart)
    is a type of control chart used to monitor either variables or attributes-type data using the monitored business
    or industrial process's entire history of output. While other control charts treat rational subgroups of samples
    individually, the EWMA chart tracks the exponentially-weighted moving average of all prior sample means.

    WIKIPEDIA: https://en.wikipedia.org/wiki/EWMA_chart
    """

    def __init__(self, alpha=0.3, coefficient=3):
        """
        :param alpha: Discount rate of ewma, usually in (0.2, 0.3).
        :param coefficient: Coefficient is the width of the control limits, usually in (2.7, 3.0).
        """
        self.alpha = alpha
        self.coefficient = coefficient

    def predict(self, X):
        """
        Predict if a particular sample is an outlier or not.

        :param X: the time series to detect of
        :param type X: pandas.Series
        :return: 1 denotes normal, 0 denotes abnormal
        """
        s = [X[0]]
        for i in range(1, len(X)):
            temp = self.alpha * X[i] + (1 - self.alpha) * s[-1]
            s.append(temp)
        s_avg = np.mean(s)
        sigma = np.sqrt(np.var(X))
        ucl = s_avg + self.coefficient * sigma * np.sqrt(self.alpha / (2 - self.alpha))
        lcl = s_avg - self.coefficient * sigma * np.sqrt(self.alpha / (2 - self.alpha))
        if s[-1] > ucl or s[-1] < lcl:
            return 0
        return 1