"""
The Tornado Framework
By Ali Pesaranghader
University of Ottawa, Ontario, Canada
E-mail: apesaran -at- uottawa -dot- ca / alipsgh -at- gmail -dot- com
---
*** The Cumulative Sum (CUSUM) Method Implementation ***
Paper: Page, Ewan S. "Continuous inspection schemes."
Published in: Biometrika 41.1/2 (1954): 100-115.
URL: http://www.jstor.org/stable/2333009
"""

from dictionary.tornado_dictionary import TornadoDic
from drift_detection.detector import SuperDetector


class CUSUM(SuperDetector):
    """The Cumulative Sum (CUSUM) drift detection method class."""

    DETECTOR_NAME = TornadoDic.CUSUM

    def __init__(self, min_instance=30, delta=0.005, lambda_=50):

        super().__init__()

        self.MINIMUM_NUM_INSTANCES = min_instance

        self.m_n = 1
        self.x_mean = 0
        self.sum = 0
        self.delta = delta
        self.lambda_ = lambda_

    def run(self, pr):

        pr = 1 if pr is False else 0

        warning_status = False
        drift_status = False

        # 1. UPDATING STATS
        self.x_mean = self.x_mean + (pr - self.x_mean) / self.m_n
        self.sum = max([0, self.sum + pr - self.x_mean - self.delta])
        self.m_n += 1

        # 2. UPDATING WARNING AND DRIFT STATUSES
        if self.m_n >= self.MINIMUM_NUM_INSTANCES:
            if self.sum > self.lambda_:
                drift_status = True

        return warning_status, drift_status

    def reset(self):
        super().reset()
        self.m_n = 1
        self.x_mean = 0
        self.sum = 0

    def get_settings(self):
        return [str(self.MINIMUM_NUM_INSTANCES) + "." + str(self.delta) + "." + str(self.lambda_),
                "$n_{min}$:" + str(self.MINIMUM_NUM_INSTANCES) + ", " +
                "$\delta$:" + str(self.delta).upper() + ", " +
                "$\lambda$:" + str(self.lambda_).upper()]

e54ff10e6fa91fb7f82f936e8d9aca4aada7d848
<mask token>


class CUSUM(SuperDetector):
    <mask token>
    <mask token>

    def __init__(self, min_instance=30, delta=0.005, lambda_=50):
        super().__init__()
        self.MINIMUM_NUM_INSTANCES = min_instance
        self.m_n = 1
        self.x_mean = 0
        self.sum = 0
        self.delta = delta
        self.lambda_ = lambda_
    <mask token>

    def reset(self):
        super().reset()
        self.m_n = 1
        self.x_mean = 0
        self.sum = 0

    def get_settings(self):
        return [str(self.MINIMUM_NUM_INSTANCES) + '.' + str(self.delta) +
            '.' + str(self.lambda_), '$n_{min}$:' + str(self.
            MINIMUM_NUM_INSTANCES) + ', ' + '$\\delta$:' + str(self.delta).
            upper() + ', ' + '$\\lambda$:' + str(self.lambda_).upper()]

<mask token>


class CUSUM(SuperDetector):
    <mask token>
    <mask token>

    def __init__(self, min_instance=30, delta=0.005, lambda_=50):
        super().__init__()
        self.MINIMUM_NUM_INSTANCES = min_instance
        self.m_n = 1
        self.x_mean = 0
        self.sum = 0
        self.delta = delta
        self.lambda_ = lambda_

    def run(self, pr):
        pr = 1 if pr is False else 0
        warning_status = False
        drift_status = False
        self.x_mean = self.x_mean + (pr - self.x_mean) / self.m_n
        self.sum = max([0, self.sum + pr - self.x_mean - self.delta])
        self.m_n += 1
        if self.m_n >= self.MINIMUM_NUM_INSTANCES:
            if self.sum > self.lambda_:
                drift_status = True
        return warning_status, drift_status

    def reset(self):
        super().reset()
        self.m_n = 1
        self.x_mean = 0
        self.sum = 0

    def get_settings(self):
        return [str(self.MINIMUM_NUM_INSTANCES) + '.' + str(self.delta) +
            '.' + str(self.lambda_), '$n_{min}$:' + str(self.
            MINIMUM_NUM_INSTANCES) + ', ' + '$\\delta$:' + str(self.delta).
            upper() + ', ' + '$\\lambda$:' + str(self.lambda_).upper()]

<mask token>


class CUSUM(SuperDetector):
    """The Cumulative Sum (CUSUM) drift detection method class."""
    DETECTOR_NAME = TornadoDic.CUSUM

    def __init__(self, min_instance=30, delta=0.005, lambda_=50):
        super().__init__()
        self.MINIMUM_NUM_INSTANCES = min_instance
        self.m_n = 1
        self.x_mean = 0
        self.sum = 0
        self.delta = delta
        self.lambda_ = lambda_

    def run(self, pr):
        pr = 1 if pr is False else 0
        warning_status = False
        drift_status = False
        self.x_mean = self.x_mean + (pr - self.x_mean) / self.m_n
        self.sum = max([0, self.sum + pr - self.x_mean - self.delta])
        self.m_n += 1
        if self.m_n >= self.MINIMUM_NUM_INSTANCES:
            if self.sum > self.lambda_:
                drift_status = True
        return warning_status, drift_status

    def reset(self):
        super().reset()
        self.m_n = 1
        self.x_mean = 0
        self.sum = 0

    def get_settings(self):
        return [str(self.MINIMUM_NUM_INSTANCES) + '.' + str(self.delta) +
            '.' + str(self.lambda_), '$n_{min}$:' + str(self.
            MINIMUM_NUM_INSTANCES) + ', ' + '$\\delta$:' + str(self.delta).
            upper() + ', ' + '$\\lambda$:' + str(self.lambda_).upper()]

<mask token>
from dictionary.tornado_dictionary import TornadoDic
from drift_detection.detector import SuperDetector


class CUSUM(SuperDetector):
    """The Cumulative Sum (CUSUM) drift detection method class."""
    DETECTOR_NAME = TornadoDic.CUSUM

    def __init__(self, min_instance=30, delta=0.005, lambda_=50):
        super().__init__()
        self.MINIMUM_NUM_INSTANCES = min_instance
        self.m_n = 1
        self.x_mean = 0
        self.sum = 0
        self.delta = delta
        self.lambda_ = lambda_

    def run(self, pr):
        pr = 1 if pr is False else 0
        warning_status = False
        drift_status = False
        self.x_mean = self.x_mean + (pr - self.x_mean) / self.m_n
        self.sum = max([0, self.sum + pr - self.x_mean - self.delta])
        self.m_n += 1
        if self.m_n >= self.MINIMUM_NUM_INSTANCES:
            if self.sum > self.lambda_:
                drift_status = True
        return warning_status, drift_status

    def reset(self):
        super().reset()
        self.m_n = 1
        self.x_mean = 0
        self.sum = 0

    def get_settings(self):
        return [str(self.MINIMUM_NUM_INSTANCES) + '.' + str(self.delta) +
            '.' + str(self.lambda_), '$n_{min}$:' + str(self.
            MINIMUM_NUM_INSTANCES) + ', ' + '$\\delta$:' + str(self.delta).
            upper() + ', ' + '$\\lambda$:' + str(self.lambda_).upper()]

"""
The Tornado Framework
By Ali Pesaranghader
University of Ottawa, Ontario, Canada
E-mail: apesaran -at- uottawa -dot- ca / alipsgh -at- gmail -dot- com
---
*** The Cumulative Sum (CUSUM) Method Implementation ***
Paper: Page, Ewan S. "Continuous inspection schemes."
Published in: Biometrika 41.1/2 (1954): 100-115.
URL: http://www.jstor.org/stable/2333009
"""

from dictionary.tornado_dictionary import TornadoDic
from drift_detection.detector import SuperDetector


class CUSUM(SuperDetector):
    """The Cumulative Sum (CUSUM) drift detection method class."""

    DETECTOR_NAME = TornadoDic.CUSUM

    def __init__(self, min_instance=30, delta=0.005, lambda_=50):

        super().__init__()

        self.MINIMUM_NUM_INSTANCES = min_instance

        self.m_n = 1
        self.x_mean = 0
        self.sum = 0
        self.delta = delta
        self.lambda_ = lambda_

    def run(self, pr):

        pr = 1 if pr is False else 0

        warning_status = False
        drift_status = False

        # 1. UPDATING STATS
        self.x_mean = self.x_mean + (pr - self.x_mean) / self.m_n
        self.sum = max([0, self.sum + pr - self.x_mean - self.delta])
        self.m_n += 1

        # 2. UPDATING WARNING AND DRIFT STATUSES
        if self.m_n >= self.MINIMUM_NUM_INSTANCES:
            if self.sum > self.lambda_:
                drift_status = True

        return warning_status, drift_status

    def reset(self):
        super().reset()
        self.m_n = 1
        self.x_mean = 0
        self.sum = 0

    def get_settings(self):
        return [str(self.MINIMUM_NUM_INSTANCES) + "." + str(self.delta) + "." + str(self.lambda_),
                "$n_{min}$:" + str(self.MINIMUM_NUM_INSTANCES) + ", " +
                "$\delta$:" + str(self.delta).upper() + ", " +
                "$\lambda$:" + str(self.lambda_).upper()]
