-
Notifications
You must be signed in to change notification settings - Fork 3
/
Program.cs
279 lines (267 loc) · 10.5 KB
/
Program.cs
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
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
using System;
using System.Collections.Generic;
using System.Globalization;
using System.IO;
using System.Linq;
using System.Threading;
using System.Threading.Tasks;
using System.Windows.Forms;
using NeuralNetwork.src;
using NeuralNetwork.test;
namespace NeuralNetwork
{
static class Program
{
/// <summary>
/// The main entry point for the application.
/// </summary>
[STAThread]
static void Main()
{
// System.Globalization.CultureInfo customCulture = (System.Globalization.CultureInfo)System.Threading.Thread.CurrentThread.CurrentCulture.Clone();
// customCulture.NumberFormat.NumberDecimalSeparator = ".";
// System.Threading.Thread.CurrentThread.CurrentCulture = customCulture;
Thread.CurrentThread.CurrentCulture = CultureInfo.GetCultureInfoByIetfLanguageTag("en-US");
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
Application.Run(new MainForm());
}
private static String path_cola = Path.GetFullPath(@"./../../data/ko.csv");
// public static void Main()
// {
// TrainPrediction();
//// Sinus();
//// TestTanhLearningOnSinus();
//// TestTanhDerivative();
//
// }
public static void TrainPrediction()
{
NNetwork network = NNetwork.SigmoidNetwork(new int[] { 5, 1 });
network.RandomizeWeights(-1, 20);
NetworkTrainer trainer = new NetworkTrainer(network);
List<double> tr = new List<double>();
for (double i = 0; i <= 1; i=i+0.05)
{
tr.Add(i);
}
double[] train_set = tr.ToArray();//new double[] { 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 };
double error = 1;
double delta = 1;
int j = 0;
for (; error > 0.01 && !(delta <= 0.00001) || j == 1; j++)
{
trainer.TrainPrediction(train_set, 0.0001, 0.2);
double new_cost = trainer.GetError();
delta = error - new_cost;
error = new_cost;
}
Console.Out.WriteLine(j+": "+error);
for (double i = 0; i <= 0.5; i=i+0.05)
{
network.SetInput(new double[] { i + 0.0, i + 0.1, i + 0.2, i + 0.3, i + 0.4 });
Show(new double[]
{
i+0.5,
network.GetOutput()[0],
// network.GetOutput()[1]
});
}
}
public static void TestTanhDerivative()
{
NNetwork n = NNetwork.HyperbolicNetwork(new int[] { 2, 2, 1 });
n.RandomizeWeights(-1, 10);
Random random = new Random();
double x;
double y;
double z;
x = random.NextDouble();
y = random.NextDouble();
z = some_function(x, y);
n.SetInput(new double[] { x, y });
n.SetAnswers(new double[] { z });
n.BackPropagate();
double[] ders = n.Derivatives();
double[] ests = n.Estimation(0.0001);
for (int i = 0; i < ders.Length; i++)
{
Show(new[]{ders[i], ests[i], ests[i]/ders[i]});
}
}
public static void TestTanhLearningOnSinus()
{
NNetwork network = NNetwork.HyperbolicNetwork(new int[] { 1, 2, 1 });
network.RandomizeWeights(1, 2);
NetworkTrainer trainer = new NetworkTrainer(network);
double[][] inputs = SinusTrainSet()[0];
double[][] outputs = SinusTrainSet()[1];
double error = 1;
double delta = 1;
int j = 0;
for (; error > 0.01 && !(delta <= 0.000001) || j == 1; j++)
{
trainer.TrainClassification(inputs, outputs);
double new_cost = trainer.GetError();
delta = error - new_cost;
error = new_cost;
}
double[][] input_test = SinusTrainSet(20)[0];
double[][] output_test = SinusTrainSet(20)[1];
trainer.IsLearning = false;
trainer.TrainClassification(input_test, output_test);
error = trainer.GetError();
Console.Out.WriteLine(error);
for (int i = 0; i < input_test.Length; i++ )
{
network.SetInput(input_test[i]);
Show(new []{input_test[i][0], network.GetOutput()[0], Math.Sin(input_test[i][0])});
}
}
// public static void M1()
// {
// GoogleParser parser = new GoogleParser();
// double[] initial_values = parser.GetDeltas(parser.ParseFile(path_cola));
// DataNormalizer normalizer = new DataNormalizer(initial_values);
// double[] normalized_values = normalizer.GetValues();
// NNetwork network = new NNetwork(new int[]{8, 8, 8, 3});
//// for(int i=0; )
// }
//
// public static void Cosinus()
// {
// NNetwork network = new NNetwork(new int[] { 1, 2, 1 });
// network.RandomizeWeights(0, 10);
// double[] inputs = SinusTrainSet()[0];
// double[] outputs = SinusTrainSet()[1];
// DataNormalizer input_normalizer = new DataNormalizer(inputs);
// DataNormalizer output_normalizer = new DataNormalizer(outputs);
// double[] n_inputs = input_normalizer.GetValues();
// double[] n_outputs = output_normalizer.GetValues();
// int max_j = 10000;
// for (int j = 0; j < max_j; j++)
// {
// for (int i = 0; i < n_inputs.Length; i++)
// {
// int k = i;
// network.SetInput(new double[] { n_inputs[k] });
// network.SetAnswers(new double[] { n_outputs[k] });
// network.BackPropagate();
// if (j == 0)
// {
// Show(n_inputs[i], n_outputs[i]);
// }
// }
// network.ApplyTraining(0.0000, 0.01);
// if (j < 10 || j%(max_j/10) == 0)
// {
// Console.Out.WriteLine(network.AccumulatedCost());
// }
// network.ResetCost();
//
// }
// Console.Out.WriteLine("AFTER");
// for (int i = 0; i < n_inputs.Length; i++)
// {
// network.SetInput(new double[] { n_inputs[i] });
// Show(n_inputs[i], network.GetOutput()[0]);
// }
// }
//
// public static void Sinus()
// {
// NNetwork network = new NNetwork(new int[] { 1, 2, 1 });
// network.RandomizeWeights(3, 2);
// double[] inputs = SinusTrainSet()[0];
// double[] outputs = SinusTrainSet()[1];
// DataNormalizer input_normalizer = new DataNormalizer(inputs);
// DataNormalizer output_normalizer = new DataNormalizer(outputs);
// double[] n_inputs = inputs;// input_normalizer.GetValues();
// double[] n_outputs = outputs;// output_normalizer.GetValues();
// int max_j = 10000;
// double error = 1;
// double delta = 1;
// int j = 0;
// for (; error > 0.01 && !(delta <= 0.0000001)||j==1; j++)
//// for (int j = 0; j<max_j; j++)
// {
// for (int i = 0; i < n_inputs.Length; i++)
// {
// int k = i;
// network.SetInput(new double[] {n_inputs[k]});
// network.SetAnswers(new double[] {n_outputs[k]});
// network.BackPropagate();
// if (j == 0)
// {
// Show(n_inputs[i], n_outputs[i]);
// }
// }
// if (j%(max_j/10) == 0)
// {
//// Console.Out.WriteLine(network.AccumulatedCost());
// }
// double new_cost = network.AccumulatedCost();
// delta = error - new_cost;
// error = new_cost;
// network.ResetCost();
// network.ApplyTraining(0.001, 2);
// }
// Console.Out.WriteLine(j+", Error: "+error+", delta: "+delta);
// Console.Out.WriteLine("AFTER");
//// for (int i = 0; i < n_inputs.Length; i++)
// for (double i = -Math.PI/2.0; i < Math.PI/2.0;i=i+0.2)
// {
//// network.SetInput(new double[] { n_inputs[i] });
// network.SetInput(new double[] { i });
// Show(i, network.GetOutput()[0]);
// }
// }
private static void Show(double[] values, int digits = 4)
{
for (int i = 0; i < values.Length; i++)
{
if (values[i] >= 0)
{
Console.Out.Write(" {0,-" + (digits + 2) + "} : ", Math.Round(values[i], digits));
}
else
{
Console.Out.Write("{0,-" + (digits + 3) + "} : ", Math.Round(values[i], digits));
}
}
Console.Out.WriteLine("");
}
private static double[][][] SinusTrainSet(int size=9)
{
double[][] inputs = new double[size][];
double[][] outputs = new double[size][];
double pi = Math.PI;
double step = pi/(double)size;
for (int i = 0; i < size; i++)
{
var value = -pi / 2 + i * step;
inputs[i] = new double[]{value};
outputs[i] = new double[]{Math.Sin(value)};
}
return new double[][][] { inputs, outputs };
}
private static double[][] CosinusTrainSet()
{
int n = 9;
double[] inputs = new double[n];
double[] outputs = new double[n];
double pi = Math.PI;
for (int i = 0; i < n; i++)
{
var value = -pi / 2 + i * pi / 8;
inputs[i] = value;
outputs[i] = Math.Cos(value);
}
return new double[][] { inputs, outputs };
}
public static double some_function(double x, double y)
{
return x * x * y + y * y;
}
}
}