AnalyticsCal-Classroom/ac_classes.py at master · AnalyticsCal/AnalyticsCal-Classroom · 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
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
import stats_team3 as st
from nonlinear_regression import NonLinearRegression as nlr

import math
from collections import namedtuple
class IndivModel:
    def __init__(self, values):
        self.values = values

    def mean(self):
        self.mean = st.mean(self.values)
        return self.mean
    def var(self):
        self.var = st.variance(self.values)
        return self.var
"""
a = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
b = [9.0, 8.0, 9.0, 12.0, 9.0, 12.0, 11.0, 7.0, 13.0, 9.0, 11.0, 10.0]

x = IndivModel(a)
y = IndivModel(b)
"""
class BiDataModel(IndivModel):

    def __init__(self,x, y):
       super(IndivModel, self).__init__()  # calls Base.__init__
       self.x = x
       self.y = y

    #global x_bar, y_bar
    def x_bar(self):
    #    global x_bar
        self.x_bar = self.x.mean
     #   x_bar = self.x_bar
        return self.x_bar
        #return self.x_bar

    def y_bar(self):
     #   global y_bar
        self.y_bar = self.y.mean
      #  y_bar = self.y_bar
        return self.y_bar

    def x_var(self):
        self.x_var = self.x.var
        return self.x_var

    def y_var(self):
        self.y_var = self.y.var
        return self.y_var

    def cov(self):
        self.cov = st.covariance(self.x.values,self.x_bar(), self.y.values, self.y_bar())
        return self.cov

    def corr_coeff(self):
        self.corr_coeff = st.correlationCoefficient(self.x.values,self.y.values)
        self.threshold = 1.96/ math.sqrt(len(self.x.values))
        return self.corr_coeff

    def nlr_coef(self):
        self.poly_coeff = []
        self.exp_coeff = []
        self.sin_coeff = []
        self.exp_transf = []

    def anova(self):
        self.msr = None
        self.mse = None
        self.ssr = None
        self.sse = None
        self.f = None
        self.p = None
        self.model_confidence = None
        self.t_m=None
        self.t_c=None

    def models(self):
        self.linear = {'eqn' : '','msr': None, 'mse': None,'ssr':None, 'sse': None,'f':None,'p':None,'coeff':None,'y_pred' : [] }
        self.poly_2 = {'eqn' : '','msr': None, 'mse': None,'ssr':None, 'sse': None,'f':None,'p':None,'coeff':None,'y_pred' : []}
        self.poly_3 = {'eqn' : '','msr': None, 'mse': None,'ssr':None, 'sse': None,'f':None,'p':None,'coeff':None,'y_pred' : []}
        self.poly_4 = {'eqn' : '','msr': None, 'mse': None,'ssr':None, 'sse': None,'f':None,'p':None,'coeff':None,'y_pred' : []}

    def pred_model(self):
        self.pred_model = []
        self.pred_eqn = None

    def outliers(self):
        self.outlier_x=[]
        self.outlier_y = []


"""
    def nlr_coeff(self):
        self.nlr_coeff =
    def anova(self):
        ...
"""
#t = DataModel(x, y)
"""
x = [[5.94, 6.00, 6.08, 6.17, 6.14, 6.09, 5.87, 5.84, 5.99, 6.12, 6.42, 6.48, 6.52, 6.64, 6.75, 6.73, 6.89, 6.98, 6.98, 7.1, 7.19, 7.29, 7.65, 7.75, 7.72, 7.67, 7.66, 7.89, 8.14, 8.21, 8.05, 7.94, 7.88, 7.79, 7.41, 7.18, 7.15, 7.27, 7.37, 7.54, 7.58, 7.62, 7.58, 7.48, 7.35, 7.19, 7.19, 7.11, 7.16, 7.22, 7.36, 7.34, 7.30],
     [5.31, 5.6, 5.49, 5.8, 5.61, 5.28, 5.19, 5.18, 5.3, 5.23, 5.64, 5.62, 5.67, 5.83, 5.53, 5.76, 6.09, 6.52, 6.68, 7.07, 7.12, 7.25, 7.85, 8.02, 7.87, 7.14, 7.2, 7.59, 7.74, 7.51, 7.46, 7.09, 6.82, 6.22, 5.61, 5.48, 4.78, 4.14, 4.64, 5.52, 5.95, 6.20, 6.03, 5.6, 5.26, 4.96, 5.28, 5.37, 5.53, 5.72, 6.04, 5.66, 5.75],
     [0.29, -0.11, 0.31, -0.19, -0.33, -0.09, -0.01, 0.12, -0.07, 0.41, -0.02, 0.05, 0.16, -0.3, 0.23, 0.33, 0.43, 0.16, 0.39, 0.05, 0.13, 0.6, 0.17, -0.15, -0.73, 0.06, 0.39, 0.15, -0.23, -0.05, -0.37, -0.27, -0.6, -0.61, -0.13, -0.7, -0.64, 0.5, 0.88, 0.43, 0.25, -0.17, -0.43, -0.34, -0.3, 0.32, 0.09, 0.16, 0.19, 0.32, -0.38, 0.09, 0.07]]

Y = [1.146,-2.443,1.497,-0.132,2.025,0.737,-1.023,-0.956,0.385,0.983,5.092,3.649,2.703,-0.271,2.055,-0.714,0.653,-0.034,-1.058,-2.051,1.451,-0.989,1.358,0.746,1.855,-1.894,0.781,-0.161,2.233,2.425,2.169,0.982,4.708,6.063,9.382,9.304,10.69,6.531,7.873,3.882,4.96,1.301,1.154,0.116,4.928,2.53,8.425,5.291,5.192,0.257,4.402,3.173,5.104,]
"""
class MultiDataModel():
    def __init__(self,x, y):
        self.x = x
        self.y = y

    def x_stats(self):
        self.no_of_indep_var = len(self.x)
        self.x_mean = [st.mean(i) for i in self.x]
        self.x_var = [st.variance(i) for i in self.x]
        self.x_std_dev = [math.sqrt(st.variance(i)) for i in self.x]

    def y_stats(self):
        self.y_mean = st.mean(self.y)
        self.y_var = st.variance(self.y)
        self.y_std_dev = math.sqrt(st.variance(self.y))

    def linear_regression_coeff(self):
        self.lin_reg_coeff = []
        self.lin_reg_eqn = ""

    def anova(self):
        self.msr = None
        self.mse = None
        self.ssr = None
        self.sse = None
        self.f = None
        self.p = None
        self.model_confidence = None

    def outliers(self):
        self.outlier_x=[]
        self.outlier_y = []

class Ts_Model():
    def __init__(self,header,values):
        self.header = header
        self.values = values