Module `minder_utils.models.outlier_detection.OutlierModels`

Expand source code

import numpy as np


class Outlier_Detector:
    def __init__(self, method='grubbs'):
        pass


class ZScore:
    def __init__(self):
        return

    def fit(self, X):
        self.mean = np.mean(X, axis=0)
        self.std = np.std(X, axis=0)
        self.std[self.std == 0] = np.nan
        return

    def decision_function(self, X):
        return np.nanmean((X - self.mean) / self.std)

Classes

class Outlier_Detector (method='grubbs')

Expand source code

class Outlier_Detector:
    def __init__(self, method='grubbs'):
        pass

class ZScore

Expand source code

class ZScore:
    def __init__(self):
        return

    def fit(self, X):
        self.mean = np.mean(X, axis=0)
        self.std = np.std(X, axis=0)
        self.std[self.std == 0] = np.nan
        return

    def decision_function(self, X):
        return np.nanmean((X - self.mean) / self.std)

Methods

def decision_function(self, X)

Expand source code

def decision_function(self, X):
    return np.nanmean((X - self.mean) / self.std)

def fit(self, X)

Expand source code

def fit(self, X):
    self.mean = np.mean(X, axis=0)
    self.std = np.std(X, axis=0)
    self.std[self.std == 0] = np.nan
    return