File size: 1,492 Bytes
ae4627d | 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 | from __future__ import division
import numpy as np
def get_y(slope, intercept, x):
return x * slope + intercept
def get_error(test_data, computed_data):
errors = []
for a, b in zip(test_data, computed_data):
diff = abs(a-b)
error = diff/a
errors.append(error)
return sum(errors)/len(errors)
def normalize(x):
x = np.array(x)
cols = x.shape[1]
rows = x.shape[0]
for j in range(cols):
column = x[:, j:j+1]
for i in range(rows):
min_a = get_min(column)
max_a = get_max(column)
den = max_a - min_a
a = x[i][j]
var = x[i][j] - min_a
n = var/den
x[i][j] = n
return x
def normalize_matrix(matrix):
least = matrix[0][0]
greatest = matrix[0][0]
matrix = np.array(matrix)
rows = matrix.shape[0]
cols = matrix.shape[1]
for i in range(rows):
for j in range(cols):
if matrix[i][j] > greatest:
greatest = matrix[i][j]
elif matrix[i][j] < least and matrix[i][j] != 0:
least = matrix[i][j]
# print least, greatest
for i in range(rows):
for j in range(cols):
matrix[i][j] = ((matrix[i][j]-least)/(greatest - least))
return matrix
def get_min(x):
f = x[0]
for i in x:
if i < f:
f = i
return f
def get_max(x):
f = 0.0
for i in x:
if i > f:
f = i
return f |