-
Notifications
You must be signed in to change notification settings - Fork 0
/
TM_conv.py
102 lines (84 loc) · 3.28 KB
/
TM_conv.py
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
from pyTsetlinMachine.tm import MultiClassConvolutionalTsetlinMachine2D
import numpy as np
from time import time
# Parameters
epochs = 50
clauses = 10000
T = 88
s = 27
k_fold_amount = 10
print("epochs = ", epochs)
print("clauses = ", clauses)
print("T = ", T)
print("s = ", s, "\n")
# Shape of the game board
shape_x = 7
shape_y = 6
shape_z = 2
print("shape_x = ", shape_x)
print("shape_y = ", shape_y)
print("shape_z = ", shape_z, "\n")
# Shape of the window for ConvTM moving around in the game board
frame_x = 4
frame_y = 4
print("frame_x = ", frame_x)
print("frame_y = ", frame_y, "\n")
X_train = np.array([])
Y_train = np.array([])
X_test = np.array([])
Y_test = np.array([])
base_path_start = "Data/KfoldDataStaticTransformed/"
base_path_end = "statickfoldcorrected.data"
# path_train = "Data/eventrain.data"
# path_test = "Data/eventest.data"
def merging_k_fold(file_amount, _clauses, _T, _s, _epochs, _frame_x, _frame_y):
results = []
for i in range(file_amount):
train_string = base_path_start + str(i) + "train" + base_path_end
test_string = base_path_start + str(i) + "test" + base_path_end
score = loading_data(train_string, test_string, _clauses, _T, _s, epochs, _frame_x, _frame_y)
results.append(score)
return results
# shape[0] = length of dataset.
# shape[1] | shape_x = length of x-axis
# shape[2] | shape_y = length of y-axis
# shape[3] | shape_z = length of z-axis(if 3D)
def loading_data(_train, _test, _clauses, _T, _s, _epochs, _frame_x, _frame_y):
print("Loading training data..")
train_data = np.loadtxt(_train, delimiter=",")
global X_train
global Y_train
X_train = train_data[:, 0:-1].reshape(train_data.shape[0], shape_x, shape_y, shape_z)
Y_train = train_data[:, -1]
print("X_train.shape[0]: ", X_train.shape[0])
print("X_train.shape[1]: ", X_train.shape[1])
print("X_train.shape[2]: ", X_train.shape[2])
print("X_train.shape[3]: ", X_train.shape[3], "\n")
print("Loading test data..")
test_data = np.loadtxt(_test, delimiter=",")
global X_test
global Y_test
X_test = test_data[:, 0:-1].reshape(test_data.shape[0], shape_x, shape_y, shape_z)
Y_test = test_data[:, -1]
print("X_test.shape[0]: ", X_test.shape[0])
print("X_test.shape[1]: ", X_test.shape[1])
print("X_test.shape[2]: ", X_test.shape[2])
print("X_test.shape[3]: ", X_test.shape[3], "\n")
return ConvTM(_clauses, _T, _s, _epochs, _frame_x, _frame_y)
def ConvTM(_clauses, _T, _s, _epochs, _frame_x, _frame_y):
print("Creating MultiClass Convolutional Tsetlin Machine.")
tm = MultiClassConvolutionalTsetlinMachine2D(_clauses, _T, _s, (_frame_x, _frame_y), boost_true_positive_feedback=0)
print("Starting ConvTM..")
print("\nAccuracy over " + str(_epochs) + " epochs:\n")
for i in range(_epochs):
start = time()
tm.fit(X_train, Y_train, epochs=1, incremental=True)
stop = time()
result = 100 * (tm.predict(X_test) == Y_test).mean()
print("#%d Accuracy: %.2f%% (%.2fs)" % (i + 1, result, stop - start))
mean_accuracy = 100 * (tm.predict(X_test) == Y_test).mean()
print("Mean Accuracy:", mean_accuracy, "\n")
print("Finished running..")
return mean_accuracy
score = merging_k_fold(k_fold_amount, clauses, T, s, epochs, frame_x, frame_y)
print(score)