Gradientenabstieg mit mehreren Ein- und Ausgaben

 1     
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21

import numpy as np
                    #toes %win fans
weights = np.array([[0.1 ,0.1, -0.3],     # hurt?
                    [0.1, 0.2,    0],     # win?
                    [0.0, 1.3,  0.1]]).T  # sad?

toes =  np.array([0.85, 0.95, 0.9, 0.9]) # normalized data
wlrec = np.array([0.65, 0.8, 0.8, 0.9])
nfans = np.array([1.2, 1.3, 0.5, 1.0])

# Input is here only the first game of the season.
input = np.array([toes[0],wlrec[0],nfans[0]])

hurt = [0.1, 0.0, 0.0, 0.1]
win  = [1,     1,   0,   1]
sad  = [0.1, 0.0, 0.1, 0.2]

# Check here only the first game
true = [hurt[0], win[0], sad[0]]

alpha = 0.1  # learning rate