Algorithm_Implementations/ML_MMM.py at main · daijingz/Algorithm_Implementations · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
# Author: Jingze Dai
# Email Address: [email protected] or [email protected]
# Github: https://github.com/daijingz
# Linkedin: https://www.linkedin.com/in/jingze-dai/
# Description: Machine Learning Algorithm: Mean, Median and Mode
import numpy
import statistics
from scipy import stats


class ML_Input:
    def __init__(self):
        """! Setting an empty tree with a central node (including values) """
        self.__data = []

    def getData(self):
        """! Get data values """
        try:
            return self.__data
        except:
            raise Exception()

    def addData(self, data):
        """! Add data values """
        try:
            if type(data) not in [int, float]:
                raise TypeError()
            self.__data = self.__data + [float(data)]
        except:
            raise Exception()

    def Mean(self):
        """! Find mean values """
        return statistics.mean(self.getData())

    def ownMean(self):
        """! Own function about finding mean values """
        if len(self.__data) == 0:
            return 0
        input1 = self.getData()
        sum1 = sum(input1)
        return sum1/len(input1)

    def Median(self):
        """! Find median values """
        return numpy.median(self.getData())

    def ownMedian(self):
        """! Own function about finding median values """
        input1 = self.getData()
        input1.sort()
        if len(input1) % 2 == 0:
            median1 = len(input1) // 2
            median2 = len(input1) // 2 + 1
            return (input1[median1] + input1[median2])/2
        else:
            median = len(input1) // 2
            return input1[median]

    def Mode(self):
        """! Find mode values """
        return stats.mode(self.getData())

    def ownMode(self):
        """! Own function about finding mode values """
        occur = 0
        output = None
        for i in self.getData():
            if self.getData().count(i) > occur:
                occur = self.getData().count(i)
                output = i
        return output

    def filter_data(self, value, distance):
        """! Find all appropriate data values with given values and range """
        if type(value) not in [int, float]:
            raise TypeError()
        if type(distance) not in [int, float]:
            raise TypeError()
        new_data = []
        for i in self.__data:
            if abs(float(i) - value) < distance:
                new_data += [i]
        self.__data = new_data

    def filter_data2(self):
        """! Find all appropriate data values with given information """
        return self