ORpKTKoVQnFhs / Public_Research (public) (License: MIT) (since 2019-12-01) (hash sha1)
This is a collection of programs related to research on my masters degree.
Clone URLs: https://rocketgit.com/user/ORpKTKoVQnFhs/Public_Research ssh://rocketgit@ssh.rocketgit.com/user/ORpKTKoVQnFhs/Public_Research git://git.rocketgit.com/user/ORpKTKoVQnFhs/Public_Research
00_simslope bite_counter master
List of commits:
Subject Hash Author Date (UTC)
Indentation 8606ab9f5e9d3b01f698a3303f5e6d39418b7bd5 Josiah_Mullins_Optiplex_Mini 2019-12-01 21:41:16
Simslope Initial commit. e9ccd3d49b4b1342341f1866a3f7a941af503b30 Josiah_Mullins_Optiplex_Mini 2019-12-01 21:24:04
Commit 8606ab9f5e9d3b01f698a3303f5e6d39418b7bd5 - Indentation
This commit changed the indentation on the programs under 00_simslope
to a more pleasing (to me) convention.
Author: Josiah_Mullins_Optiplex_Mini
Author date (UTC): 2019-12-01 21:41
Committer name: Josiah_Mullins_Optiplex_Mini
Committer date (UTC): 2019-12-01 21:41
Parent(s): e9ccd3d49b4b1342341f1866a3f7a941af503b30
Signing key:
Tree: 79c8a6967ed2e35eee6a4dbd2d6e60c4bcc6b3b1
File Lines added Lines deleted
00_simslope/simslope.c 84 94
00_simslope/simslope0.py 8 10
00_simslope/simslope1.py 11 11
File 00_simslope/simslope.c changed (mode: 100644) (index f97d055..062672f)
1 1
2 /*
3 ** Generates simulated data of two classes.
4 ** Measurements come from 1 axis.
5 ** Class 0 has a generally downard slope, class 1 has a generally
6 ** upward slope.
7 ** The NN must learn weights that correspond to the difference
8 ** in slope (printing out the weights at end makes this clear).
9 */
2 /*
3 ** Generates simulated data of two classes.
4 ** Measurements come from 1 axis.
5 ** Class 0 has a generally downard slope, class 1 has a generally
6 ** upward slope.
7 ** The NN must learn weights that correspond to the difference
8 ** in slope (printing out the weights at end makes this clear).
9 */
10 10
11 11 #include <stdio.h> #include <stdio.h>
12 12 #include <stdlib.h> #include <stdlib.h>
 
... ... static int rand_j, rand_k; /* used for normal dist */
19 19
20 20 unsigned urand0 (void) unsigned urand0 (void)
21 21 { {
22 if (--rand_j == 0) rand_j = 55;
23 if (--rand_k == 0) rand_k = 55;
24 return rand_x[rand_k] += rand_x[rand_j];
22 if (--rand_j == 0) rand_j = 55;
23 if (--rand_k == 0) rand_k = 55;
24 return rand_x[rand_k] += rand_x[rand_j];
25 25 } }
26 26
27 27 void init_generators(unsigned seed) void init_generators(unsigned seed)
28 28 { {
29 int i;
30
31 /* inits the normal generator */
32 rand_x[1] = 1;
33 if(seed)
34 rand_x[2] = seed;
35 else
36 rand_x[2] = time (NULL);
37 for (i=3; i<56; ++i) rand_x[i] = rand_x[i-1] + rand_x[i-2];
38 rand_j = 24;
39 rand_k = 55;
40 for (i=255; i>=0; --i)
41 urand0 (); //run loop for a while
42 for (i=255; i>=0; --i)
43 rand_y[i] = urand0 ();
44 rand_z = urand0 ();
45
46 /* inits the uniform generator */
47 srand( (unsigned)time( NULL ) );
29 int i;
30
31 /* inits the normal generator */
32 rand_x[1] = 1;
33 if(seed) rand_x[2] = seed;
34 else rand_x[2] = time (NULL);
35 for (i=3; i<56; ++i) rand_x[i] = rand_x[i-1] + rand_x[i-2];
36 rand_j = 24;
37 rand_k = 55;
38 for (i=255; i>=0; --i) urand0 (); //run loop for a while
39 for (i=255; i>=0; --i) rand_y[i] = urand0 ();
40 rand_z = urand0 ();
41
42 /* inits the uniform generator */
43 srand( (unsigned)time( NULL ) );
44 return;
48 45 } }
49 46
50 47 unsigned urand (void) unsigned urand (void)
51 48 { {
52 int i;
53
54 i = rand_z % 256;
55 rand_z = rand_y[i];
56 if (--rand_j == 0) rand_j = 55;
57 if (--rand_k == 0) rand_k = 55;
58 rand_y[i] = rand_x[rand_k] += rand_x[rand_j];
59 return rand_z;
49 int i;
50
51 i = rand_z % 256;
52 rand_z = rand_y[i];
53 if (--rand_j == 0) rand_j = 55;
54 if (--rand_k == 0) rand_k = 55;
55 rand_y[i] = rand_x[rand_k] += rand_x[rand_j];
56 return rand_z;
60 57 } }
61 58
62 59
63 60
64 61
65 /* returns a value from a normal dist with mean=0 stddev=1 */
66
62 /* returns a value from a normal dist with mean=0 stddev=1 */
67 63 double normal_rand (void) double normal_rand (void)
68 64 { {
69 static int flag = 0;
70 static double z, a = 2147483648.0;
71 double v1, v2, s;
72
73 if (flag) {
74 flag = 0;
75 return z;
76 }
77 flag = 1;
78 do {
79 v1 = urand()/a - 1;
80 v2 = urand()/a - 1;
81 }
82 while ((s = v1*v1 + v2*v2) > 1.0);
83 s = sqrt (-2.0 * log(s) / s);
84 z = v1 * s;
85 return v2 * s;
65 static int flag = 0;
66 static double z, a = 2147483648.0;
67 double v1, v2, s;
68
69 if (flag) {
70 flag = 0;
71 return z;
72 }
73 flag = 1;
74 do {
75 v1 = urand()/a - 1;
76 v2 = urand()/a - 1;
77 }
78 while ((s = v1*v1 + v2*v2) > 1.0);
79 s = sqrt (-2.0 * log(s) / s);
80 z = v1 * s;
81 return v2 * s;
86 82 } }
87 83
88
89
90 /* returns a value from -4...+4 */
91
84 /* returns a value from -4...+4 */
92 85 double uniform_rand (void) double uniform_rand (void)
93 86 { {
94 int randgen;
95 double ret;
87 int randgen;
88 double ret;
96 89
97 randgen=rand();
98 ret=((double)randgen/(double)RAND_MAX*8.0)-4.0;
99 return ret;
90 randgen=rand();
91 ret=(((double)randgen)/((double)RAND_MAX)*8.0)-4.0;
92 return ret;
100 93 } }
101 94
102 95
103 96 #define DATA 1000 #define DATA 1000
104 97 #define SAMPLES_PER_DATA 20 #define SAMPLES_PER_DATA 20
105 98
106 int main()
107
99 int main(void)
108 100 { {
109 int i,j;
110 int d,slope;
111 double base,n,step;
112
113 init_generators(0);
114
115 for (j=0; j<DATA; j++)
116 {
117 n=normal_rand();
118 if (n < 0.0)
119 slope=-1;
120 else
121 slope=1;
122 n=normal_rand();
123 base=n*100.0+500.0;
124 printf("%d\t%d",(slope == -1 ? 0 : 1),(int)base);
125 for (d=1; d<SAMPLES_PER_DATA; d++)
126 {
127 n=normal_rand();
128 step=n*3.0+(double)slope*2; /* 3.0 => low noise, 10.0 => high noise */
129 base+=step;
130 printf("\t%d",(int)base);
131 }
132 printf("\n");
133 }
134
101 int i,j;
102 int d,slope;
103 double base,n,step;
104
105 init_generators(0);
106
107 for (j=0; j<DATA; j++)//This loop generates {$DATA} datasets
108 {
109 n=normal_rand();
110 if (n < 0.0) slope=-1;
111 else slope=1;
112 n=normal_rand();
113 base=n*100.0+500.0;
114 printf("%d\t%d",(slope == -1 ? 0 : 1),(int)base);
115 for (d=1; d<SAMPLES_PER_DATA; d++)//This loop gnereates datasets of size {$SAMPLES_PER_DATA}.
116 {
117 n=normal_rand();
118 step=n*3.0+(double)slope*2; /* 3.0 => low noise, 10.0 => high noise */
119 base+=step;
120 printf("\t%d",(int)base);
121 }
122 printf("\n");
123 }
124 return 0;
135 125 } }
File 00_simslope/simslope0.py changed (mode: 100644) (index ae8e351..d53b86a)
... ... for a in range(0,len(d)):
36 36 # normalize each row of data # normalize each row of data
37 37 normdata=[] normdata=[]
38 38 for a in range(0,len(data)): for a in range(0,len(data)):
39 norm=[]
40 s=min(data[a])
41 t=max(data[a])
42 for b in range(0,len(data[a])):
43 norm.append((float(data[a][b])-float(s))/(float(t)-float(s)))
44 normdata.append(norm)
39 norm=[]
40 s=min(data[a])
41 t=max(data[a])
42 for b in range(0,len(data[a])):
43 norm.append((float(data[a][b])-float(s))/(float(t)-float(s)))
44 normdata.append(norm)
45 45
46 46 #print(normdata) #print(normdata)
47 47
 
... ... model = keras.Sequential([
60 60 # cannot have InputLayer() or network will not save/load (bug in TF) # cannot have InputLayer() or network will not save/load (bug in TF)
61 61 # instead, specify input_shape in first real layer # instead, specify input_shape in first real layer
62 62 # keras.layers.InputLayer(input_shape=(20,)), # keras.layers.InputLayer(input_shape=(20,)),
63 keras.layers.Dense(2, input_shape=(20,), activation='sigmoid')
64 ])
63 keras.layers.Dense(2, input_shape=(20,), activation='sigmoid')])
65 64
66 65 model.compile(optimizer='adam', model.compile(optimizer='adam',
67 66 loss='sparse_categorical_crossentropy', loss='sparse_categorical_crossentropy',
68 67 metrics=['accuracy']) metrics=['accuracy'])
69 68
70 69 print("Training") print("Training")
71 metrics = model.fit(data, classes, epochs=100, verbose=2,
72 validation_split=0.2)
70 metrics = model.fit(data, classes, epochs=100, verbose=2,validation_split=0.2)
73 71 print("Max value: ", max(metrics.history['acc']), " at epoch", np.argmax(metrics.history['acc'])) print("Max value: ", max(metrics.history['acc']), " at epoch", np.argmax(metrics.history['acc']))
74 72
75 73 print("Testing") print("Testing")
File 00_simslope/simslope1.py changed (mode: 100644) (index 6e36081..61d98e6)
... ... classes=[]
17 17 data=[] data=[]
18 18
19 19 for a in range(0,len(d)): for a in range(0,len(d)):
20 classes.append(d[a][0])
21 row=[]
22 for b in range(1,len(d[a])):
23 row.append(d[a][b])
24 data.append(row)
20 classes.append(d[a][0])
21 row=[]
22 for b in range(1,len(d[a])):
23 row.append(d[a][b])
24 data.append(row)
25 25
26 26 #print(classes) #print(classes)
27 27 #print(data) #print(data)
 
... ... for a in range(0,len(d)):
29 29 # normalize each row of data # normalize each row of data
30 30 normdata=[] normdata=[]
31 31 for a in range(0,len(data)): for a in range(0,len(data)):
32 norm=[]
33 s=min(data[a])
34 t=max(data[a])
35 for b in range(0,len(data[a])):
36 norm.append((float(data[a][b])-float(s))/(float(t)-float(s)))
37 normdata.append(norm)
32 norm=[]
33 s=min(data[a])
34 t=max(data[a])
35 for b in range(0,len(data[a])):
36 norm.append((float(data[a][b])-float(s))/(float(t)-float(s)))
37 normdata.append(norm)
38 38
39 39 #print(normdata) #print(normdata)
40 40
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