Hugging Face's logo

Spaces:
amirhosseinkarami
/
SFL
Runtime error

App Files Community
SFL
File size: 8,648 Bytes 
bae498f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
import numpy as np


def get_simple_data(num_points, max_domain):
    full_data = []
    for i in range(num_points):
        new_datum = [np.random.uniform(0, max_domain),
                     np.random.uniform(0, max_domain),
                     np.random.uniform(0, max_domain)]
        full_data.append(new_datum)
    return full_data


def get_simple_var_names():
    return ["a", "b", "c"]


def get_xy_var_names():
    return ["x", "y"]


def get_angle_data(num_data):
    full_data = []
    for i in range(num_data):
        theta = np.random.uniform(0, 2 * np.pi)
        while np.abs(theta - np.pi / 2) < 1e-3 or np.abs(theta - 3 * np.pi / 2) < 1e-3:
            theta = np.random.uniform(0, 2 * np.pi)
        new_datum = [np.sin(theta), np.cos(theta), np.tan(theta)]
        full_data.append(new_datum)
    return full_data


def get_fake_angles(num_data):
    full_data = []
    for i in range(num_data):
        [s, c] = np.random.uniform(-1, 1, 2)
        t = np.random.uniform(-5, 5)
        new_datum = [s, c, t]
        full_data.append(new_datum)
    return full_data


def get_xy_data(num_data, max_domain):
    full_data = []
    for i in range(num_data):
        # t = np.random.uniform(0, 2 * np.pi)
        # r = 2 + 3 * np.cos(t)
        # # r = 5 * np.cos(2*t)/np.cos(t)
        #
        # x = r * np.cos(t)
        # y = r * np.sin(t)

        y = np.random.uniform(-3, 2)
        sign = np.random.choice([-1, 1])
        x = sign * np.sqrt(np.abs(2 * np.sin(y) + 5 - y ** 3))
        full_data.append([x, y])
    return full_data


def get_fake_xy_data(num_data, max_domain):
    full_data = []
    for i in range(num_data):
        [x, y] = np.random.uniform(0, max_domain, 2)
        full_data.append([x, y])
    return full_data



def get_triangle_data(num_triangles, max_domain):
    full_data = []
    for i in range(num_triangles):
        [x1, y1, x2, y2, x3, y3] = np.random.uniform(0, max_domain, 6)

        a = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)
        b = np.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
        c = np.sqrt((x1 - x3) ** 2 + (y1 - y3) ** 2)

        va = np.arccos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c))
        vb = np.arccos((c ** 2 + a ** 2 - b ** 2) / (2 * a * c))
        vc = np.arccos((a ** 2 + b ** 2 - c ** 2) / (2 * b * a))

        sa, sb, sc = np.sin([va, vb, vc])
        ca, cb, cc = np.cos([va, vb, vc])
        ta, tb, tc = np.tan([va, vb, vc])

        new_datum = []
        # new_datum.extend([x1, x2, x3])
        # new_datum.extend([y1, y2, y3])
        new_datum.extend([a, b, c])
        # new_datum.extend([va, vb, vc])
        # new_datum.extend([sa, sb, sc])
        # new_datum.extend([ca, cb, cc])
        # new_datum.extend([ta, tb, tc])

        full_data.append(new_datum)
    return full_data


def get_right_triangle_data(num_triangles, max_domain):
    full_data = []
    for i in range(num_triangles):
        [s1, s2] = np.random.uniform(0, max_domain, 2)
        c = max(s1, s2)
        a = min(s1, s2)
        b = np.sqrt(c * c - a * a)

        va = np.arccos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c))
        vb = np.arccos((c ** 2 + a ** 2 - b ** 2) / (2 * a * c))
        vc = np.arccos((a ** 2 + b ** 2 - c ** 2) / (2 * b * a))

        sa, sb, sc = np.sin([va, vb, vc])
        ca, cb, cc = np.cos([va, vb, vc])
        ta, tb, tc = np.tan([va, vb, vc])

        new_datum = []
        new_datum.extend([a, b, c])
        # new_datum.extend([sa, sb, sc])
        # new_datum.extend([ca, cb, cc])
        # new_datum.extend([ta, tb, tc])

        full_data.append(new_datum)

    return full_data


def get_fake_triangle_data(num_triangles, max_domain):
    full_data = []
    for i in range(num_triangles):
        [a, b, c] = np.random.uniform(0, max_domain, 3)
        [sa, sb, sc] = np.random.uniform(-1, 1, 3)
        [ca, cb, cc] = np.random.uniform(-1, 1, 3)
        [ta, tb, tc] = np.random.uniform(-5, 5, 3)

        new_datum = []
        new_datum.extend([a, b, c])
        # new_datum.extend([va, vb, vc])
        new_datum.extend([sa, sb, sc])
        new_datum.extend([ca, cb, cc])
        new_datum.extend([ta, tb, tc])

        # new_datum.extend([va, vb, vc])
        # new_datum.extend([a, 2*a])
        # new_datum.extend([ta, tb, tc])

        full_data.append(new_datum)
    return full_data


def get_non_triangle_data(num_triangles, max_domain):
    full_data = []
    for i in range(num_triangles):
        [x1, y1, x2, y2, x3, y3, x4, y4] = np.random.uniform(0, max_domain, 8)

        a = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)
        b = np.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
        c = np.sqrt((x4 - x3) ** 2 + (y4 - y3) ** 2)
        d = np.sqrt((x1 - x4) ** 2 + (y1 - y4) ** 2)
        e24 = np.sqrt((x4 - x2) ** 2 + (y4 - y2) ** 2)
        e13 = np.sqrt((x3 - x1) ** 2 + (y3 - y1) ** 2)

        va = np.arccos((b ** 2 + c ** 2 - e24 ** 2) / (2 * b * c))
        vb = np.arccos((d ** 2 + c ** 2 - e13 ** 2) / (2 * d * c))
        vc = np.arccos((d ** 2 + a ** 2 - e24 ** 2) / (2 * d * a))

        sa, sb, sc = np.sin([va, vb, vc])
        ca, cb, cc = np.cos([va, vb, vc])
        ta, tb, tc = np.tan([va, vb, vc])

        new_datum = []
        new_datum.extend([a, b, c])
        # new_datum.extend([va, vb, vc])
        new_datum.extend([sa, sb, sc])
        new_datum.extend([ca, cb, cc])
        # new_datum.extend([ta, tb, tc])
        full_data.append(new_datum)
    return full_data

def get_var_names():
    # var_names = ["a", "b", "c"]
    var_names = []
    var_names.extend(["sin(x)", "cos(x)", "tan(x)"])
    # var_names.extend(["A", "B", "C"])
    var_names.extend(["sin(A)", "sin(B)", "sin(C)"])
    var_names.extend(["cos(A)", "cos(B)", "cos(C)"])
    var_names.extend(["tan(A)", "tan(B)", "tan(C)"])
    return var_names
#
# our_results = []
#
# settings.true_eqn = "0*x1"
# settings.num_features = num_smp_features
# settings.show_output = False
# settings.keep_logs = False
#
# model = SFL()
#
# for trial_round in range(num_trials):
#     sampled_features = np.random.choice(range(len(full_data[0])), num_smp_features, replace=True)
#
#     # sampled_features = [3,6]
#     data = [[row[smp_i] for smp_i in sampled_features] for row in full_data]
#     smp_var_names = [var_names[smp_i] for smp_i in sampled_features]
#     print("Trial round {} of {}.".format(trial_round + 1, num_trials))
#     print("  Using variables {}.".format(smp_var_names))
#
#     settings.fixed_x = []
#     settings.fixed_y = []
#     for line in data:
#         settings.fixed_x.append(line)
#         settings.fixed_y.append(0)
#
#     model.reset(var_names=smp_var_names)
#
#     # train_X = DataUtils.generate_data(settings.train_N, n_vars=model.n_input_variables,
#     #                                       avoid_zero=settings.avoid_zero)
#     # valid_X = DataUtils.generate_data(settings.train_N, n_vars=model.n_input_variables,
#     #                                   avoid_zero=settings.avoid_zero)
#     # test_X = DataUtils.generate_data(settings.test_N, n_vars=model.n_input_variables,
#     #                                  min_x=settings.test_scope[0],
#     #                                  max_x=settings.test_scope[1])
#     train_X = np.array(data)
#     valid_X = np.array(data)
#     test_X = np.array(data)
#
#     train_X = train_X.reshape([-1, settings.num_dims_per_feature, settings.num_features])
#     valid_X = valid_X.reshape([-1, settings.num_dims_per_feature, settings.num_features])
#     test_X = test_X.reshape([-1, settings.num_dims_per_feature, settings.num_features])
#
#     start_time = time.time()
#     best_model, best_iter, best_err = model.repeat_train(train_X,
#                                                          settings.num_train_repeat_processes,
#                                                          test_x=test_X)
#     running_time = time.time() - start_time
#
#     print("best_model: {}".format(best_model))
#     print("----------------------")
#     print("Finished this experiment. Took {:.2f} minutes.\n".format(running_time / 60))
#
#     our_results.append([best_err, best_model, smp_var_names])
#     our_results = sorted(our_results, key=lambda entry: entry[0])
#
#     output_file = open("images/triangle_output.txt", "w")
#     for entry in our_results:
#         output_file.write("{}\n{}\n{}\n\n".format(entry[0], entry[2], entry[1]))
#     output_file.close()
#
#     print("Final solution found at attempt {}:".format(best_iter))
#     print("y = {}".format(best_model))
#     print("Test error:  {}".format(best_err))
#     if best_err < 0.02:
#         print("Attained error less than 0.02 - great!")
#     print()