CoCalc -- Grupo_10

GitHub Repository: robertopucp/1eco35_2022_2
Path: blob/main/Trabajo_grupal/WG3/Grupo_10_spyder.py
²⁷¹⁴ views
1
# -*- coding: utf-8 -*-
2
"""
3
Created on Sat Sep 17 16:46:11 2022
4

5
@author: User
6
"""
7
import numpy as np
8
import pandas as pd
9
from pandas import DataFrame, Series
10
import statistics
11
import inspect  # Permite conocer los argumentos de una función , classes, etc.
12
from random import *
13

14
#-------------------------------------------------------------------------
15
#                            Pregunta 2
16
#-------------------------------------------------------------------------
17

18

19
# Creamos un vector "a" con 100 observaciones
20
#--------------------------------------------
21
vector = np.arange(100)
22

23
#Reescalamos el vector con la funcion dada
24
#----------------------------------------
25
# 1era forma
26
def rescale(x):
27
    
28
    out = (x - min(x))/(max(x)-min(x))
29
    
30
    return out 
31

32
list( map( lambda x: rescale(x) , vector) )  
33

34
# 2da forma
35
def rescale(x,max,min):
36
    
37
    out = (x - min(x))/(max(x)-min(x))
38
    
39
    return out 
40

41
map( lambda x, v1 = max(vector), v2 = min(vector): rescale(X,v1, v2) , vector)
42

43
list( map( lambda x, v1 = max(vector), v2 = min(vector): rescale(x,v1, v2) , vector)  )  
44

45

46
vector1 = (vector - min(vector))/(max(vector)-min(vector))
47

48
# Creamos una matriz de 100x50
49
#------------------------------
50
np.random.seed(15632)
51
X1 = np.random.rand(100)
52
print(X1) 
53
X2 = np.random.rand(100) 
54
X3 = np.random.rand(100) 
55
X4 = np.random.rand(100) 
56
X5 = np.random.rand(100) 
57
X6 = np.random.rand(100) 
58
X7 = np.random.rand(100) 
59
X8 = np.random.rand(100) 
60
X9 = np.random.rand(100) 
61
X10 = np.random.rand(100) 
62
X11 = np.random.rand(100) 
63
X12 = np.random.rand(100) 
64
X13 = np.random.rand(100) 
65
X14 = np.random.rand(100) 
66
X15 = np.random.rand(100) 
67
X16 = np.random.rand(100) 
68
X17 = np.random.rand(100) 
69
X18 = np.random.rand(100) 
70
X19 = np.random.rand(100) 
71
X20 = np.random.rand(100) 
72
X21 = np.random.rand(100) 
73
X22 = np.random.rand(100) 
74
X23 = np.random.rand(100) 
75
X24 = np.random.rand(100) 
76
X25 = np.random.rand(100) 
77
X26 = np.random.rand(100) 
78
X27 = np.random.rand(100) 
79
X28 = np.random.rand(100) 
80
X29 = np.random.rand(100) 
81
X30 = np.random.rand(100) 
82
X31 = np.random.rand(100) 
83
X32 = np.random.rand(100) 
84
X33 = np.random.rand(100) 
85
X34 = np.random.rand(100) 
86
X35 = np.random.rand(100) 
87
X36 = np.random.rand(100) 
88
X37 = np.random.rand(100) 
89
X38 = np.random.rand(100) 
90
X39 = np.random.rand(100) 
91
X40 = np.random.rand(100) 
92
X41 = np.random.rand(100) 
93
X42 = np.random.rand(100) 
94
X43 = np.random.rand(100) 
95
X44 = np.random.rand(100) 
96
X45 = np.random.rand(100) 
97
X46 = np.random.rand(100) 
98
X47 = np.random.rand(100) 
99
X48 = np.random.rand(100) 
100
X49 = np.random.rand(100) 
101
X50 = np.random.rand(100) 
102

103

104

105
X = np.column_stack((np.ones(100),X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14,X15,X16,X17,X18,X19,X20,X21,X22,X23,X24,X25,X26,X27,X28,X29,X30,X31,X32,X33,X34,X35,X36,X37,X38,X39,X40,X41,X42,X43,X44,X45,X46,X47,X48,X49,X50))
106
print (X)
107

108
#Reescalamos la matriz con la funcion dada
109
#----------------------------------------
110
X_res = np.apply_along_axis(lambda X: (X - min(X))/(max(X)-min(X)),0, X)
111
print (X_res)
112

113

114
#-------------------------------------------------------------------------
115
#                            Pregunta 3
116
#-------------------------------------------------------------------------
117

118
#args
119
#------
120

121
#Reescalamos el vector con la funcion dada
122

123
# 1era forma
124
def rescalev( *args ):
125
    
126
    minimo = np.min(vector)
127
    
128
    maximo = np.max(vector)
129
    
130
    result = list( map( lambda x: (x-min(x))/(max(x)-min(x)) , vector)   ) 
131
    
132
    return result
133

134

135
rescalev(vector)
136

137
# Estandarizamos el vector
138

139
def standarizev( *args ):
140
    
141
    mean = np.mean(vector)
142
    
143
    std = np.std(vector)
144
    
145
    result = list( map( lambda x: (x- np.mean(x))/np.std(x) , vector)   ) 
146
 
147
    
148
    return result
149

150

151
standarizev(vector)
152

153

154
#kwargs
155
#------
156

157
def calculator( *list_vars, **kwargs):
158
    if ( kwargs[ 'function' ] == "reescale" ) :
159
        
160
        # Get the first value
161
        result = np.apply_along_axis(lambda X: (X - min(X))/(max(X)-min(X)),0, X)
162
    
163
    elif ( kwargs[ 'function' ] == "standarize" ) :
164

165
        result = np.apply_along_axis(lambda x: (x-x.mean())/x.std(),0, X)
166
    else:
167
        raise ValueError( f"The function argument {kwargs[ 'function' ]} is not supported." )
168
        
169
        # Mensaje de error por tipo de argumento
170

171
    return result
172

173
calculator( X, function = "reescale" )
174
calculator( X, function = "standarize" )
175

176

177

178
Product

Resources

Company
