├── ANN_module.py
├── LICENSE
├── r.landslide.html
└── r.landslide.py


/ANN_module.py:
--------------------------------------------------------------------------------
  1 | # -*- coding: utf-8 -*-
  2 | """
  3 | Created on Fri Oct 19 16:52:59 2018
  4 | 
  5 | @author: Lucimara Bragagnolo
  6 | """
  7 | 
  8 | def ann_module(input_data,output_data,reckon,num_hidden,coef,nr_epochs,val_samples,test_samples,directory,columnst,col,row,flag_train):
  9 |     
 10 |     import numpy as np
 11 |     import math
 12 |     import matplotlib
 13 |     matplotlib.use('agg')
 14 |     import matplotlib.pyplot as plt
 15 |     import os
 16 |     import grass.script as gscript
 17 | 
 18 |     os.mkdir(directory) #Create directory
 19 | 
 20 |     #Scaling the data
 21 |     max_in = np.zeros((1,input_data.shape[1]))
 22 |     min_in = np.zeros((1,input_data.shape[1]))  
 23 |     max_out = 1
 24 |     min_out = 0
 25 | 
 26 |     
 27 |     alldata = np.concatenate((input_data,reckon))
 28 |     
 29 |     for i in range(0,input_data.shape[1]):
 30 |         max_in[0,i] = np.nanmax(alldata[:,i])
 31 |         min_in[0,i] = np.nanmin(alldata[:,i])
 32 |         
 33 |     os.chdir(directory)
 34 |     np.savetxt('max_in.txt', (max_in), delimiter=',')
 35 |     np.savetxt('min_in.txt', (min_in), delimiter=',')
 36 |     os.chdir('../')
 37 | 
 38 |     for j in range(0,input_data.shape[1]):
 39 |         if max_in[0,j] != 0:
 40 |             input_data[:,j] = (input_data[:,j] - min_in[0,j])/(max_in[0,j]-min_in[0,j])
 41 |     
 42 |     for j in range(0,reckon.shape[1]): 
 43 |         if max_in[0,j] != 0:
 44 |             reckon[:,j] = (reckon[:,j] - min_in[0,j])/(max_in[0,j]-min_in[0,j])
 45 | 
 46 |     #Resample data
 47 |     m = 1
 48 |     n = input_data.shape[0]
 49 |     n_test = int(math.ceil(test_samples*n)) #TORNAR GENERICA ESSAS %%
 50 |     n_val = int(math.ceil(val_samples*n))
 51 |     n_train = int(n - n_test - n_val)
 52 |     reg_set = np.arange(0,n) 
 53 |     T = np.zeros((n_train,1))
 54 |     V = np.zeros((n_val,1))
 55 | 
 56 |     flag_test = 0
 57 |     B_ind = ((n-m)*np.random.rand(2*n_test,1)+m)
 58 |     B_ind = [math.floor(x) for x in B_ind] 
 59 |         
 60 |     while flag_test == 0:
 61 |         if len(np.unique(B_ind)) == n_test:
 62 |             flag_test = 1
 63 |         else:
 64 |             del B_ind[0] 
 65 |     B_ind = np.unique(B_ind)
 66 |     B_ind = B_ind.astype(int)
 67 |     B = reg_set[B_ind]-1 #Test
 68 |     
 69 |     reg_rest = np.setdiff1d(reg_set,B) #Dataset for TRAINING and VALIDATIONS 
 70 |     
 71 |     flag_val = 0
 72 |     V_buff = ((n-n_test-m)*np.random.rand(2*n_val,1)+m)
 73 |     V_buff = [math.floor(x) for x in V_buff]
 74 |     while flag_val == 0:
 75 |         if len(np.unique(V_buff)) == n_val:
 76 |             flag_val = 1
 77 |         elif len(np.unique(V_buff)) > n_val:
 78 |             del V_buff[0]
 79 |         else:
 80 |             V_buff = ((n-n_test-m)*np.random.rand(2*n_val,1)+m)
 81 |             V_buff = [math.floor(x) for x in V_buff]
 82 |                         
 83 |     V_buff = [int(x) for x in V_buff] #Transform to integer
 84 |     V[:,0] = (reg_rest[np.unique(V_buff)])
 85 |     T[:,0] = np.setdiff1d(reg_rest,V[:,0])
 86 |     
 87 |     T = T.astype(np.int64) #Training
 88 |     V = V.astype(np.int64) #Validations
 89 |     B = B.astype(np.int64) #Test
 90 |     p = input_data.shape[1]
 91 |     input_test = np.zeros((B.shape[0],p)) 
 92 |     output_test = np.zeros((B.shape[0],1))
 93 |     input_train = np.zeros((T.shape[0],p)) 
 94 |     output_train = np.zeros((T.shape[0],1))
 95 |     input_val = np.zeros((B.shape[0],p)) 
 96 |     output_val = np.zeros((T.shape[0],1))
 97 |     
 98 |     n1 = T.shape[0]
 99 |     n2 = B.shape[0]
100 |     n3 = V.shape[0]
101 |             
102 |     for i in range(0,n1-1):
103 |         input_train[i,:] = input_data[T[i],:]
104 |         output_train[i,:] = output_data[T[i]]
105 |             
106 |     for i in range(0,n2-1):
107 |         input_test[i,:] = input_data[B[i],:]
108 |         output_test[i,:] = output_data[B[i]]
109 |     
110 |     for i in range(0,n3-1):
111 |         input_val[i,:] = input_data[V[i],:]
112 |         output_val[i,:] = output_data[V[i]]
113 |     
114 |     output_data = output_data.reshape((output_data.size,1))
115 |     
116 |     #Validations
117 |     def ann_validate(input_data,output_data,weights,peights,biasH,biasO,max_in,max_out,min_in,min_out):
118 |         #Collect dimensions
119 |         num_input = input_data.shape[1] #Number of parameters
120 |         reg_size = input_data.shape[0] #Number of records
121 |         num_output = output_data.shape[1]
122 |         num_hidden = peights.shape[0] - 1 #Number of lines of weights matrix of the output layer - 1
123 |     
124 |         S = np.zeros((num_hidden,1))
125 |         H = np.zeros((num_hidden,1))
126 |         R = np.zeros((num_output,1))
127 |         output = np.zeros((reg_size,num_output))
128 |         erro = np.zeros((1,reg_size))
129 |     
130 |         def activation(x): #Sigmoid as activation function
131 |             fx = 1/(1+math.exp(-x))
132 |             return fx
133 |     
134 |         for k in range(0,reg_size):
135 |             for i in range(0,num_hidden):
136 |                 for j in range(0,num_input):
137 |                     S[i,0] = S[i,0] + input_data[k,j]*weights[j,i] 
138 |                 S[i,0] = S[i,0] + biasH[i,0]*weights[num_input,i]
139 |                 H[i,0] = activation(S[i,0])
140 |                 S[i,0] = 0
141 |         
142 |             for i in range(0,num_output): 
143 |                 for j in range(0,num_hidden):
144 |                     R[i,0] = R[i,0] + H[j,0]*peights[j,i]
145 |                 R[i,0] = R[i,0] + biasO[i,0]*peights[num_hidden,i] 
146 |                 output[k,i] = activation(R[i,0])
147 |                 R[i,0] = 0   
148 |     
149 |             erro[0,k] = output[k,:] - output_data[k,:]
150 |     
151 |         return output,erro
152 |     
153 |     #Test
154 |     def ann_test(input_data,output_data,weights,peights,biasH,biasO):
155 |         
156 |         #Collect dimensions
157 |         num_input = input_data.shape[1]
158 |         reg_size = input_data.shape[0]
159 |         num_output = output_data.shape[1]
160 |         num_hidden = peights.shape[0] - 1
161 |         
162 |         def activation(x): #activation function
163 |             fx = 1/(1+math.exp(-x))
164 |             return fx
165 |             
166 |         S = np.zeros((num_hidden,1))
167 |         H = np.zeros((num_hidden,1))
168 |         R = np.zeros((num_output,1))
169 |         output = np.zeros((reg_size,num_output))
170 |         erro_dec = np.zeros((1,reg_size))
171 |         erro_round = np.zeros((1,reg_size))
172 |         
173 |         for k in range(0,reg_size):
174 |             for i in range(0,num_hidden):
175 |                 for j in range(0,num_input):
176 |                     S[i,0] = S[i,0] + input_data[k,j]*weights[j,i]            
177 |                 S[i,0] = S[i,0] + biasH[i,0]*weights[num_input,i]
178 |                 H[i,0] = activation(S[i,0])
179 |                 S[i,0] = 0
180 |                 
181 |             for i in range(0,num_output):
182 |                 for j in range(0,num_hidden):
183 |                     R[i,0] = R[i,0] + H[j,0]*peights[j,i]
184 |                 R[i,0] = R[i,0] + biasO[i,0]*peights[num_hidden,i] 
185 |                 output[k,i] = activation(R[i,0])
186 |                 R[i,0] = 0
187 |                 
188 |             erro_dec[0,k] = output[k,:] - output_data[k,:]
189 |             erro_round[0,k] = np.around(erro_dec[0,k])
190 |     
191 |         return output,erro_dec,erro_round
192 |     
193 |     #Reckon
194 |     def ann_reckon(input_data,weights,peights,biasH,biasO):
195 |         
196 |         #Collect dimensions
197 |         num_input = input_data.shape[1]
198 |         reg_size = input_data.shape[0]
199 |         num_output = 1
200 |         num_hidden = peights.shape[0] - 1
201 |         
202 |         def activation(x):
203 |             fx = 1/(1+math.exp(-x))
204 |             return fx
205 |             
206 |         S = np.zeros((num_hidden,1))
207 |         H = np.zeros((num_hidden,1))
208 |         R = np.zeros((num_output,1))
209 |         output = np.zeros((reg_size,num_output))
210 |     
211 |         for k in range(0,reg_size):
212 |             for i in range(0,num_hidden):
213 |                 for j in range(0,num_input):
214 |                     S[i,0] = S[i,0] + input_data[k,j]*weights[j,i]            
215 |                 S[i,0] = S[i,0] + biasH[i,0]*weights[num_input,i]
216 |                 H[i,0] = activation(S[i,0])
217 |                 S[i,0] = 0
218 |                 
219 |             for i in range(0,num_output):
220 |                 for j in range(0,num_hidden):
221 |                     R[i,0] = R[i,0] + H[j,0]*peights[j,i]
222 |                 R[i,0] = R[i,0] + biasO[i,0]*peights[num_hidden,i] 
223 |                 output[k,i] = activation(R[i,0])
224 |                 R[i,0] = 0
225 |                 
226 |         output_reckon = output
227 |         
228 |         return output_reckon
229 |     
230 |     #Training
231 |     num_input = input_train.shape[1] #Number of parameters
232 |     reg_size = input_train.shape[0] #Number of examples
233 |     num_output = output_train.shape[1] #Number of outputs (in this case, just 1 - susceptibility)
234 |     weights = np.random.rand(num_input+1,num_hidden) 
235 |     peights = np.random.rand(num_hidden+1,num_output)
236 |     
237 |     biasH = np.ones((num_hidden,1))
238 |     biasO = np.ones((num_output,1))
239 |     S = np.zeros((num_hidden,1))
240 |     H = np.zeros((num_hidden,1))
241 |     R = np.zeros((num_output,1))
242 |     output = np.zeros((reg_size,num_output))
243 |     erro = np.zeros((1,nr_epochs)) 
244 |     erro_train = np.zeros((1,nr_epochs))
245 |     erro_validate = np.zeros((1,nr_epochs)) 
246 |     
247 |     def activation(x): 
248 |         fx = 1/(1+math.exp(-x))
249 |         return fx
250 |     
251 |     for epoch in range(0,nr_epochs):
252 |         for k in range(0,reg_size):
253 |             for i in range(0,num_hidden):
254 |                 for j in range(0,num_input):
255 |                     S[i,0] = S[i,0] + input_train[k,j]*weights[j,i]
256 |                 S[i,0] = S[i,0] + biasH[i,0]*weights[num_input,i]
257 |                 H[i,0] = activation(S[i,0])
258 |                 S[i,0] = 0
259 |                 
260 |             for i in range(0,num_output):
261 |                 for j in range(0,num_hidden):
262 |                     R[i,0] = R[i,0] + H[j,0]*peights[j,i]
263 |                 R[i,0] = R[i,0] + biasO[i,0]*peights[num_hidden,i]
264 |                 output[k,i] = activation(R[i,0])
265 |                 R[i,0] = 0
266 |                 
267 |             #Backpropagation
268 |             #Uptade the weights in the output layer
269 |             for i in range(0,num_hidden):
270 |                 for j in range(0,num_output):
271 |                     if i < (num_hidden+1):
272 |                         peights[i,j] = peights[i,j] + coef*(output_train[k,j] - output[k,j])*output[k,j]*(1-output[k,j])*H[i,0]
273 |                     elif i == (num_hidden+1):
274 |                         peights[i,j] = peights[i,j] + coef*(output_train[k,j] - output[k,j])*output[k,j]*(1-output[k,j])*biasO[j,0]
275 |                         
276 |             #Uptade the weights in the hidden layer
277 |             buff = 0
278 |             for j in range(0,num_hidden):
279 |                 for i in range(0,num_input):
280 |                     for k1 in range(0,num_output):
281 |                         buff = buff + (output_train[k,k1] - output[k,k1])*output[k,k1]*(1-output[k,k1])*peights[j,k1]
282 |                     
283 |                     if i < (num_input+1):
284 |                         weights[i,j] = weights[i,j] + coef*buff*H[j,0]*(1-H[j,0])*input_train[k,i]
285 |                     elif i == num_input+1:
286 |                         weights[i,j] = weights[i,j] + coef*buff*H[j,i]*(1-H[j,0])*biasH[j,0]
287 |                         
288 |                     buff = 0 #Zeroes the buffer variable
289 |             
290 |         erro_train[0,epoch] =  np.linalg.norm((output - output_train)/reg_size) 
291 |         output_val,erro_val = ann_validate(input_val,output_val,weights,peights,biasH,biasO,max_in,max_out,min_in,min_out)
292 |         erro_validate[0,epoch] = np.linalg.norm(erro_val)
293 |         
294 |         #Collects the weights in the minimum validation error
295 |         if epoch == 0:
296 |             W = weights
297 |             P = peights
298 |             epoch_min = epoch
299 |             erro_min = 1000
300 |         elif erro_min > np.linalg.norm(erro_validate[0,epoch]):
301 |             W = weights
302 |             P = peights
303 |             epoch_min = epoch
304 |             erro_min = np.linalg.norm(erro_validate[0,epoch])
305 |         
306 |     [output,erro_test,erro_round] = ann_test(input_test,output_test,weights,peights,biasH,biasO)
307 |     
308 |     x = np.arange(1,nr_epochs+1).reshape((nr_epochs,1))
309 |     plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.5, hspace=None)
310 |     plt.subplot(1,2,1)
311 |     plt.plot(x,erro_train.T,x,erro_validate.T,'g-', epoch_min, erro_min,'g*')
312 |     plt.xlabel('Epochs')
313 |     plt.ylabel('Root mean square output error')
314 |     plt.legend(('Training','Validation','Early stop'))
315 |     plt.subplot(1,2,2)
316 |     plt.bar(np.arange(1,(erro_test.shape[1])+1),erro_test.reshape(erro_test.shape[1]))
317 |     plt.xlabel('Instances')
318 |     plt.ylabel('Error (ANN output - real output)')
319 | 
320 |     os.chdir(directory)
321 |     plt.savefig('ANN_train_val', dpi=300, facecolor='w', edgecolor='w',
322 |         orientation='portrait', papertype=None, format=None,
323 |         transparent=False, bbox_inches='tight', pad_inches=0.2,
324 |         frameon=None)
325 |     os.chdir('../')
326 |         
327 |     rounded = np.asarray(erro_round).reshape((B.shape[0],1))
328 |     error = sum(abs(erro_round.T))
329 |     gscript.message(_("Test set error: "))
330 |     gscript.message(error)
331 |     
332 |     #Assign the minimum weights to their original names
333 |     weights = W
334 |     peights = P
335 | 
336 |     os.chdir(directory)
337 |     np.savetxt('weights.txt', (weights), delimiter=',')
338 |     np.savetxt('peights.txt', (peights), delimiter=',')
339 |     np.savetxt('biasH.txt', (biasH), delimiter=',')
340 |     np.savetxt('biasO.txt', (biasO), delimiter=',')
341 |     os.mkdir('Inputs and outputs')
342 |     os.chdir('Inputs and outputs')
343 |     np.savetxt('Input_test.txt', (input_test), delimiter=',')
344 |     np.savetxt('Output_test.txt', (output_test), delimiter=',')
345 |     np.savetxt('Input_val.txt', (input_val), delimiter=',')
346 |     np.savetxt('Output_val.txt', (output_val), delimiter=',')
347 |     np.savetxt('Input_train.txt', (input_train), delimiter=',')
348 |     np.savetxt('Output_train.txt', (output_train), delimiter=',')
349 |     np.savetxt('Error_train.txt', (erro_train), delimiter=',')
350 |     np.savetxt('Error_val.txt', (erro_validate), delimiter=',')
351 |     np.savetxt('Epoch_and_error_min.txt', (epoch_min,erro_min), delimiter=',')
352 |     np.savetxt('Test_set_TOTAL_error.txt', (error), delimiter=',')
353 |     np.savetxt('Error_test.txt', (erro_test), delimiter=',')
354 |     param = open('ANN_Parameters.txt','w')
355 |     param.write('Hidden neurons: '+str(num_hidden))
356 |     param.write('\n Learning rate: '+str(coef))
357 |     param.write('\n Epochs: '+str(nr_epochs))
358 |     param.close()
359 |     os.chdir('../')
360 |     os.chdir('../')
361 |     
362 |     #Sensitivity analysis
363 |     def sensitivity(input_data,output_size,weights,peights,biasH,biasO):
364 |         
365 |         input_size = input_data.shape[1] #Number of columns (parameters)
366 |         npts = 200 #Number of samples in the sensitivity evaluation set
367 |         sens_set = np.random.random_sample((npts,1)) #Return random floats in the half-open interval [0.0, 1.0)
368 |         fixed_par_value = np.empty([input_size,1])
369 |         ones_sens = np.ones((200,1))
370 |         
371 |         #Calculation of the normalized mean value of each entry
372 |         for k in range(0,input_size): 
373 |             fixed_par_value[k] = (np.mean(input_data[:,k]))
374 |     
375 |         #Pre-allocation
376 |         input_sens = [[([1] * npts) for j in range(input_size)] for i in range(input_size)]
377 |         output_sens = [[([0] * npts) for j in range(input_size)] for i in range(input_size)]
378 |         input_sens = np.asarray(input_sens,dtype=float)
379 |         
380 |         for k1 in range(0,input_size):
381 |             for k2 in range(0,input_size):
382 |                 if k1 == k2:
383 |                     input_sens[k1,k2,:] = sens_set.reshape(sens_set.shape[0]).T
384 |                 else:
385 |                     input_sens[k1,k2,:] = (ones_sens*fixed_par_value[k2]).reshape(ones_sens.shape[0])
386 |     
387 |         for k1 in range(0,input_size):
388 |             input_sens2 = np.asarray(input_sens[k1]).T
389 |             output = ann_reckon(input_sens2,weights,peights,biasH,biasO)
390 |             output_sens[k1] = output
391 |     
392 |         return sens_set,fixed_par_value,input_sens,output_sens
393 |     
394 |     [sens_set,fixed_par_value,input_sens,output_sens] = sensitivity(input_data,output_data.shape[1],weights,peights,biasH,biasO)
395 |     for k in range(0,input_data.shape[1]):
396 |         plt.figure()
397 |         plt.plot(sens_set,output_sens[k],'.')
398 |         plt.title('Sensitivity analysis. Parameter: '+columnst[k]) 
399 |         plt.ylabel('Output response')
400 |         plt.xlabel('Parameter: '+columnst[k])
401 | 
402 |         os.chdir(directory)
403 |         plt.savefig('SensitivityAnalysisVar_'+columnst[k], dpi=300, facecolor='w', edgecolor='w',
404 |             orientation='portrait', papertype=None, format=None,
405 |             transparent=False, bbox_inches='tight', pad_inches=0.2,
406 |             frameon=None)
407 |         os.chdir('../') 
408 |     
409 |     #Reckon
410 |     if not flag_train:  
411 |         output_reckon = ann_reckon(reckon,weights,peights,biasH,biasO)
412 |     
413 |         a = np.reshape(output_reckon,(col,row),order='F') 
414 |         a = np.transpose(a)
415 |     else:
416 |         a = 0
417 |     
418 |     return a
419 | 
420 | #Reckon
421 | def ann_reckon(input_data,weights,peights,biasH,biasO):
422 |     
423 |     import numpy as np
424 |     import math
425 | 
426 |     #Collect dimensions
427 |     num_input = input_data.shape[1]
428 |     reg_size = input_data.shape[0]
429 |     num_output = 1
430 |     num_hidden = peights.shape[0] - 1
431 |     
432 |     def activation(x): 
433 |         fx = 1/(1+math.exp(-x))
434 |         return fx
435 |         
436 |     S = np.zeros((num_hidden,1))
437 |     H = np.zeros((num_hidden,1))
438 |     R = np.zeros((num_output,1))
439 |     output = np.zeros((reg_size,num_output))
440 | 
441 |     for k in range(0,reg_size):
442 |         for i in range(0,num_hidden):
443 |             for j in range(0,num_input):
444 |                 S[i,0] = S[i,0] + input_data[k,j]*weights[j,i]            
445 |             S[i,0] = S[i,0] + biasH[i,0]*weights[num_input,i]
446 |             H[i,0] = activation(S[i,0])
447 |             S[i,0] = 0
448 |             
449 |         for i in range(0,num_output):
450 |             for j in range(0,num_hidden):
451 |                 R[i,0] = R[i,0] + H[j,0]*peights[j,i]
452 |             R[i,0] = R[i,0] + biasO[i,0]*peights[num_hidden,i] 
453 |             output[k,i] = activation(R[i,0])
454 |             R[i,0] = 0
455 |             
456 |     output_reckon = output
457 |     
458 |     return output_reckon
459 | 
460 | def ANN_batch(input_data,output_data,reckon,hidden,trials,coef,nr_epochs,val_samples,test_samples,directory,columnst,col,row,flag_train):
461 | 
462 |     #hidden is a vector now
463 |     #trials is the number of initial conditions
464 |     #Train a set of neural networks and select the best one
465 |     #Different number of hidden neurons
466 |     #Different number of initial conditions
467 | 
468 |     import numpy as np
469 |     import math
470 |     import matplotlib
471 |     matplotlib.use('agg')
472 |     import matplotlib.pyplot as plt
473 |     import os
474 |     import grass.script as gscript
475 | 
476 |     os.mkdir(directory) #Create directory -> REVER HERE
477 | 
478 |     #Scaling the data
479 |     max_in = np.zeros((1,input_data.shape[1]))
480 |     min_in = np.zeros((1,input_data.shape[1]))  
481 |     max_out = 1
482 |     min_out = 0
483 | 
484 |     
485 |     alldata = np.concatenate((input_data,reckon))
486 |     
487 |     for i in range(0,input_data.shape[1]):
488 |         max_in[0,i] = np.nanmax(alldata[:,i])
489 |         min_in[0,i] = np.nanmin(alldata[:,i])
490 |         
491 |     os.chdir(directory)
492 |     np.savetxt('max_in.txt', (max_in), delimiter=',')
493 |     np.savetxt('min_in.txt', (min_in), delimiter=',')
494 |     os.chdir('../')
495 | 
496 |     for j in range(0,input_data.shape[1]):
497 |         if max_in[0,j] != 0:
498 |             input_data[:,j] = (input_data[:,j] - min_in[0,j])/(max_in[0,j]-min_in[0,j])
499 |     
500 |     for j in range(0,reckon.shape[1]): 
501 |         if max_in[0,j] != 0:
502 |             reckon[:,j] = (reckon[:,j] - min_in[0,j])/(max_in[0,j]-min_in[0,j])
503 | 
504 |     #Resample data
505 |     m = 1
506 |     n = input_data.shape[0]
507 |     n_test = int(math.ceil(test_samples*n)) #TORNAR GENERICA ESSAS %%
508 |     n_val = int(math.ceil(val_samples*n))
509 |     n_train = int(n - n_test - n_val)
510 |     reg_set = np.arange(0,n) 
511 |     T = np.zeros((n_train,1))
512 |     V = np.zeros((n_val,1))
513 | 
514 |     flag_test = 0
515 |     B_ind = ((n-m)*np.random.rand(2*n_test,1)+m)
516 |     B_ind = [math.floor(x) for x in B_ind] 
517 |         
518 |     while flag_test == 0:
519 |         if len(np.unique(B_ind)) == n_test:
520 |             flag_test = 1
521 |         else:
522 |             del B_ind[0] 
523 |     B_ind = np.unique(B_ind)
524 |     B_ind = B_ind.astype(int)
525 |     B = reg_set[B_ind]-1 #Test
526 |     
527 |     reg_rest = np.setdiff1d(reg_set,B) #Dataset for TRAINING and VALIDATIONS 
528 |     
529 |     flag_val = 0
530 |     V_buff = ((n-n_test-m)*np.random.rand(2*n_val,1)+m)
531 |     V_buff = [math.floor(x) for x in V_buff]
532 |     while flag_val == 0:
533 |         if len(np.unique(V_buff)) == n_val:
534 |             flag_val = 1
535 |         elif len(np.unique(V_buff)) > n_val:
536 |             del V_buff[0]
537 |         else:
538 |             V_buff = ((n-n_test-m)*np.random.rand(2*n_val,1)+m)
539 |             V_buff = [math.floor(x) for x in V_buff]
540 |                         
541 |     V_buff = [int(x) for x in V_buff] #Transform to integer
542 |     V[:,0] = (reg_rest[np.unique(V_buff)])
543 |     T[:,0] = np.setdiff1d(reg_rest,V[:,0])
544 |     
545 |     T = T.astype(np.int64) #Training
546 |     V = V.astype(np.int64) #Validations
547 |     B = B.astype(np.int64) #Test
548 |     p = input_data.shape[1]
549 |     input_test = np.zeros((B.shape[0],p)) 
550 |     output_test = np.zeros((B.shape[0],1))
551 |     input_train = np.zeros((T.shape[0],p)) 
552 |     output_train = np.zeros((T.shape[0],1))
553 |     input_val = np.zeros((B.shape[0],p)) 
554 |     output_val = np.zeros((T.shape[0],1))
555 |     
556 |     n1 = T.shape[0]
557 |     n2 = B.shape[0]
558 |     n3 = V.shape[0]
559 |             
560 |     for i in range(0,n1-1):
561 |         input_train[i,:] = input_data[T[i],:]
562 |         output_train[i,:] = output_data[T[i]]
563 |             
564 |     for i in range(0,n2-1):
565 |         input_test[i,:] = input_data[B[i],:]
566 |         output_test[i,:] = output_data[B[i]]
567 |     
568 |     for i in range(0,n3-1):
569 |         input_val[i,:] = input_data[V[i],:]
570 |         output_val[i,:] = output_data[V[i]]
571 |     
572 |     output_data = output_data.reshape((output_data.size,1))
573 |     
574 |     def ann_train(input_train,output_train,input_val,output_val,input_test,output_test,reckon,hidden,trials,coef,nr_epochs,val_samples,test_samples,directory,columnst,col,row,flag_train):
575 |         #Training
576 |         num_input = input_train.shape[1] #Number of parameters
577 |         reg_size = input_train.shape[0] #Number of examples
578 |         num_output = output_train.shape[1] #Number of outputs (in this case, just 1 - susceptibility)
579 |         weights = np.random.rand(num_input+1,num_hidden) 
580 |         peights = np.random.rand(num_hidden+1,num_output)
581 |         
582 |         biasH = np.ones((num_hidden,1))
583 |         biasO = np.ones((num_output,1))
584 |         S = np.zeros((num_hidden,1))
585 |         H = np.zeros((num_hidden,1))
586 |         R = np.zeros((num_output,1))
587 |         output = np.zeros((reg_size,num_output))
588 |         erro = np.zeros((1,nr_epochs)) 
589 |         erro_train = np.zeros((1,nr_epochs))
590 |         erro_validate = np.zeros((1,nr_epochs)) 
591 |         
592 |         def activation(x): 
593 |             fx = 1/(1+math.exp(-x))
594 |             return fx
595 |         
596 |         for epoch in range(0,nr_epochs):
597 |             for k in range(0,reg_size):
598 |                 for i in range(0,num_hidden):
599 |                     for j in range(0,num_input):
600 |                         S[i,0] = S[i,0] + input_train[k,j]*weights[j,i]
601 |                     S[i,0] = S[i,0] + biasH[i,0]*weights[num_input,i]
602 |                     H[i,0] = activation(S[i,0])
603 |                     S[i,0] = 0
604 |                     
605 |                 for i in range(0,num_output):
606 |                     for j in range(0,num_hidden):
607 |                         R[i,0] = R[i,0] + H[j,0]*peights[j,i]
608 |                     R[i,0] = R[i,0] + biasO[i,0]*peights[num_hidden,i]
609 |                     output[k,i] = activation(R[i,0])
610 |                     R[i,0] = 0
611 |                     
612 |                 #Backpropagation
613 |                 #Uptade the weights in the output layer
614 |                 for i in range(0,num_hidden):
615 |                     for j in range(0,num_output):
616 |                         if i < (num_hidden+1):
617 |                             peights[i,j] = peights[i,j] + coef*(output_train[k,j] - output[k,j])*output[k,j]*(1-output[k,j])*H[i,0]
618 |                         elif i == (num_hidden+1):
619 |                             peights[i,j] = peights[i,j] + coef*(output_train[k,j] - output[k,j])*output[k,j]*(1-output[k,j])*biasO[j,0]
620 |                             
621 |                 #Uptade the weights in the hidden layer
622 |                 buff = 0
623 |                 for j in range(0,num_hidden):
624 |                     for i in range(0,num_input):
625 |                         for k1 in range(0,num_output):
626 |                             buff = buff + (output_train[k,k1] - output[k,k1])*output[k,k1]*(1-output[k,k1])*peights[j,k1]
627 |                         
628 |                         if i < (num_input+1):
629 |                             weights[i,j] = weights[i,j] + coef*buff*H[j,0]*(1-H[j,0])*input_train[k,i]
630 |                         elif i == num_input+1:
631 |                             weights[i,j] = weights[i,j] + coef*buff*H[j,i]*(1-H[j,0])*biasH[j,0]
632 |                             
633 |                         buff = 0 #Zeroes the buffer variable
634 |                 
635 |             erro_train[0,epoch] =  np.linalg.norm((output - output_train)/reg_size) 
636 |             output_val,erro_val = ann_validate(input_val,output_val,weights,peights,biasH,biasO,max_in,max_out,min_in,min_out)
637 |             erro_validate[0,epoch] = np.linalg.norm(erro_val)
638 |             
639 |             #Collects the weights in the minimum validation error
640 |             if epoch == 0:
641 |                 W = weights
642 |                 P = peights
643 |                 epoch_min = epoch
644 |                 erro_min = 1000
645 |             elif erro_min > np.linalg.norm(erro_validate[0,epoch]):
646 |                 W = weights
647 |                 P = peights
648 |                 epoch_min = epoch
649 |                 erro_min = np.linalg.norm(erro_validate[0,epoch])
650 |             
651 |         [output,erro_test,erro_round] = ann_test(input_test,output_test,weights,peights,biasH,biasO)
652 |         
653 |         error = np.linalg.norm(erro_test)
654 |         rounded = np.asarray(erro_round).reshape((B.shape[0],1))
655 |         error_total = sum(abs(erro_round.T))
656 |         
657 |         #Assign the minimum weights to their original names
658 |         weights = W
659 |         peights = P
660 |         
661 |         return output,weights,peights,biasH,biasO,erro_train,erro_validate,epoch_min,erro_min,error,erro_test,error_total     
662 | 
663 |     #Validations
664 |     def ann_validate(input_data,output_data,weights,peights,biasH,biasO,max_in,max_out,min_in,min_out):
665 |         #Collect dimensions
666 |         num_input = input_data.shape[1] #Number of parameters
667 |         reg_size = input_data.shape[0] #Number of records
668 |         num_output = output_data.shape[1]
669 |         num_hidden = peights.shape[0] - 1 #Number of lines of weights matrix of the output layer - 1
670 |     
671 |         S = np.zeros((num_hidden,1))
672 |         H = np.zeros((num_hidden,1))
673 |         R = np.zeros((num_output,1))
674 |         output = np.zeros((reg_size,num_output))
675 |         erro = np.zeros((1,reg_size))
676 |     
677 |         def activation(x): #Sigmoid as activation function
678 |             fx = 1/(1+math.exp(-x))
679 |             return fx
680 |     
681 |         for k in range(0,reg_size):
682 |             for i in range(0,num_hidden):
683 |                 for j in range(0,num_input):
684 |                     S[i,0] = S[i,0] + input_data[k,j]*weights[j,i] 
685 |                 S[i,0] = S[i,0] + biasH[i,0]*weights[num_input,i]
686 |                 H[i,0] = activation(S[i,0])
687 |                 S[i,0] = 0
688 |         
689 |             for i in range(0,num_output): 
690 |                 for j in range(0,num_hidden):
691 |                     R[i,0] = R[i,0] + H[j,0]*peights[j,i]
692 |                 R[i,0] = R[i,0] + biasO[i,0]*peights[num_hidden,i] 
693 |                 output[k,i] = activation(R[i,0])
694 |                 R[i,0] = 0   
695 |     
696 |             erro[0,k] = output[k,:] - output_data[k,:]
697 |     
698 |         return output,erro
699 |     
700 |     #Test
701 |     def ann_test(input_data,output_data,weights,peights,biasH,biasO):
702 |         
703 |         #Collect dimensions
704 |         num_input = input_data.shape[1]
705 |         reg_size = input_data.shape[0]
706 |         num_output = output_data.shape[1]
707 |         num_hidden = peights.shape[0] - 1
708 |         
709 |         def activation(x): #activation function
710 |             fx = 1/(1+math.exp(-x))
711 |             return fx
712 |             
713 |         S = np.zeros((num_hidden,1))
714 |         H = np.zeros((num_hidden,1))
715 |         R = np.zeros((num_output,1))
716 |         output = np.zeros((reg_size,num_output))
717 |         erro_dec = np.zeros((1,reg_size))
718 |         erro_round = np.zeros((1,reg_size))
719 |         
720 |         for k in range(0,reg_size):
721 |             for i in range(0,num_hidden):
722 |                 for j in range(0,num_input):
723 |                     S[i,0] = S[i,0] + input_data[k,j]*weights[j,i]            
724 |                 S[i,0] = S[i,0] + biasH[i,0]*weights[num_input,i]
725 |                 H[i,0] = activation(S[i,0])
726 |                 S[i,0] = 0
727 |                 
728 |             for i in range(0,num_output):
729 |                 for j in range(0,num_hidden):
730 |                     R[i,0] = R[i,0] + H[j,0]*peights[j,i]
731 |                 R[i,0] = R[i,0] + biasO[i,0]*peights[num_hidden,i] 
732 |                 output[k,i] = activation(R[i,0])
733 |                 R[i,0] = 0
734 |                 
735 |             erro_dec[0,k] = output[k,:] - output_data[k,:]
736 |             erro_round[0,k] = np.around(erro_dec[0,k])
737 |     
738 |         return output,erro_dec,erro_round
739 |     
740 |     #Reckon
741 |     def ann_reckon(input_data,weights,peights,biasH,biasO):
742 |         
743 |         #Collect dimensions
744 |         num_input = input_data.shape[1]
745 |         reg_size = input_data.shape[0]
746 |         num_output = 1
747 |         num_hidden = peights.shape[0] - 1
748 |         
749 |         def activation(x):
750 |             fx = 1/(1+math.exp(-x))
751 |             return fx
752 |             
753 |         S = np.zeros((num_hidden,1))
754 |         H = np.zeros((num_hidden,1))
755 |         R = np.zeros((num_output,1))
756 |         output = np.zeros((reg_size,num_output))
757 |     
758 |         for k in range(0,reg_size):
759 |             for i in range(0,num_hidden):
760 |                 for j in range(0,num_input):
761 |                     S[i,0] = S[i,0] + input_data[k,j]*weights[j,i]            
762 |                 S[i,0] = S[i,0] + biasH[i,0]*weights[num_input,i]
763 |                 H[i,0] = activation(S[i,0])
764 |                 S[i,0] = 0
765 |                 
766 |             for i in range(0,num_output):
767 |                 for j in range(0,num_hidden):
768 |                     R[i,0] = R[i,0] + H[j,0]*peights[j,i]
769 |                 R[i,0] = R[i,0] + biasO[i,0]*peights[num_hidden,i] 
770 |                 output[k,i] = activation(R[i,0])
771 |                 R[i,0] = 0
772 |                 
773 |         output_reckon = output
774 |         
775 |         return output_reckon
776 |     
777 |     num_hidden = len(hidden)
778 |     erro_buff = 9999999
779 | 
780 |     for k1 in range(0,num_hidden): #hidden neurons tested
781 |         for k2 in range(0,trials): #initial conditions
782 | 
783 |             [output,Weights,Peights,BiasH,BiasO,erro_train,erro_validate,epoch_min,erro_min,error,erro_test,error_total] = ann_train(input_train,output_train,input_val,output_val,input_test,output_test,reckon,hidden,trials,coef,nr_epochs,val_samples,test_samples,directory,columnst,col,row,flag_train)
784 |             
785 |             gscript.message(_("Hidden neuron: "))
786 |             neuron_tested = hidden[k1]
787 |             gscript.message(neuron_tested)
788 |             gscript.message(_("Initial condition: "))
789 |             gscript.message(k2+1)
790 |             gscript.message(_("---------------------------------"))
791 | 
792 |             if error < erro_buff:
793 |                 erro_buff = error
794 |                 #then save the data in variables
795 |                 weights = Weights 
796 |                 peights = Peights
797 |                 biasH = BiasH
798 |                 biasO = BiasO
799 |                 Erro_train = erro_train
800 |                 Erro_val = erro_validate
801 |                 Early_stop = np.array([epoch_min,erro_min])
802 |                 Erro_test = erro_test
803 |                 Erro_test_norm = erro_buff
804 |                 neurons = hidden[k1]
805 |                 Epoch_min = epoch_min
806 |                 Erro_min = erro_min
807 |                 Total_erro = error_total
808 | 
809 |     gscript.message(_("Test set error from the best ANN: "))
810 |     gscript.message(Total_erro)
811 |     gscript.message(_("Best ANN hidden neurons: "))
812 |     gscript.message(neurons)
813 | 
814 |     x = np.arange(1,nr_epochs+1).reshape((nr_epochs,1))
815 |     plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.5, hspace=None)
816 |     plt.subplot(1,2,1)
817 |     plt.plot(x,Erro_train.T,x,Erro_val.T,'g-', Epoch_min, Erro_min,'g*')
818 |     plt.xlabel('Epochs')
819 |     plt.ylabel('Root mean square output error')
820 |     plt.legend(('Training','Validation','Early stop'))
821 |     plt.subplot(1,2,2)
822 |     plt.bar(np.arange(1,(Erro_test.shape[1])+1),Erro_test.reshape(Erro_test.shape[1]))
823 |     plt.xlabel('Instances')
824 |     plt.ylabel('Error (ANN output - real output)')
825 | 
826 |     os.chdir(directory)
827 |     plt.savefig('ANN_train_val', dpi=300, facecolor='w', edgecolor='w',
828 |         orientation='portrait', papertype=None, format=None,
829 |         transparent=False, bbox_inches='tight', pad_inches=0.2,
830 |         frameon=None)
831 |     os.chdir('../')
832 |     
833 |     #Sensitivity analysis
834 |     def sensitivity(input_data,output_size,weights,peights,biasH,biasO):
835 |         
836 |         input_size = input_data.shape[1] #Number of columns (parameters)
837 |         npts = 200 #Number of samples in the sensitivity evaluation set
838 |         sens_set = np.random.random_sample((npts,1)) #Return random floats in the half-open interval [0.0, 1.0)
839 |         fixed_par_value = np.empty([input_size,1])
840 |         ones_sens = np.ones((200,1))
841 |         
842 |         #Calculation of the normalized mean value of each entry
843 |         for k in range(0,input_size): 
844 |             fixed_par_value[k] = (np.mean(input_data[:,k]))
845 |     
846 |         #Pre-allocation
847 |         input_sens = [[([1] * npts) for j in range(input_size)] for i in range(input_size)]
848 |         output_sens = [[([0] * npts) for j in range(input_size)] for i in range(input_size)]
849 |         input_sens = np.asarray(input_sens,dtype=float)
850 |         
851 |         for k1 in range(0,input_size):
852 |             for k2 in range(0,input_size):
853 |                 if k1 == k2:
854 |                     input_sens[k1,k2,:] = sens_set.reshape(sens_set.shape[0]).T
855 |                 else:
856 |                     input_sens[k1,k2,:] = (ones_sens*fixed_par_value[k2]).reshape(ones_sens.shape[0])
857 |     
858 |         for k1 in range(0,input_size):
859 |             input_sens2 = np.asarray(input_sens[k1]).T
860 |             output = ann_reckon(input_sens2,weights,peights,biasH,biasO)
861 |             output_sens[k1] = output
862 |     
863 |         return sens_set,fixed_par_value,input_sens,output_sens
864 |     
865 |     [sens_set,fixed_par_value,input_sens,output_sens] = sensitivity(input_data,output_data.shape[1],weights,peights,biasH,biasO)
866 |     for k in range(0,input_data.shape[1]):
867 |         plt.figure()
868 |         plt.plot(sens_set,output_sens[k],'.')
869 |         plt.title('Sensitivity analysis. Parameter: '+columnst[k]) 
870 |         plt.ylabel('Output response')
871 |         plt.xlabel('Parameter: '+columnst[k])
872 | 
873 |         os.chdir(directory)
874 |         plt.savefig('SensitivityAnalysisVar_'+columnst[k], dpi=300, facecolor='w', edgecolor='w',
875 |             orientation='portrait', papertype=None, format=None,
876 |             transparent=False, bbox_inches='tight', pad_inches=0.2,
877 |             frameon=None)
878 |         os.chdir('../') 
879 |     
880 |     os.chdir(directory)
881 |     np.savetxt('weights.txt', (weights), delimiter=',')
882 |     np.savetxt('peights.txt', (peights), delimiter=',')
883 |     np.savetxt('biasH.txt', (biasH), delimiter=',')
884 |     np.savetxt('biasO.txt', (biasO), delimiter=',')
885 |     os.mkdir('Inputs and outputs')
886 |     os.chdir('Inputs and outputs')
887 |     np.savetxt('Input_test.txt', (input_test), delimiter=',')
888 |     np.savetxt('Output_test.txt', (output_test), delimiter=',')
889 |     np.savetxt('Input_val.txt', (input_val), delimiter=',')
890 |     np.savetxt('Output_val.txt', (output_val), delimiter=',')
891 |     np.savetxt('Input_train.txt', (input_train), delimiter=',')
892 |     np.savetxt('Output_train.txt', (output_train), delimiter=',')
893 |     np.savetxt('Error_train.txt', (Erro_train), delimiter=',')
894 |     np.savetxt('Error_val.txt', (Erro_val), delimiter=',')
895 |     np.savetxt('Epoch_and_error_min.txt', (Early_stop), delimiter=',')
896 |     np.savetxt('Test_set_TOTAL_error.txt', (Total_erro), delimiter=',')
897 |     np.savetxt('Error_test.txt', (Erro_test), delimiter=',')
898 |     param = open('ANN_Parameters.txt','w')
899 |     param.write('Hidden neurons of best ANN: '+str(neurons))
900 |     param.write('\n Learning rate: '+str(coef))
901 |     param.write('\n Epochs: '+str(nr_epochs))
902 |     param.write('\n Hidden neurons tested: '+str(hidden))
903 |     param.write('\n Number of initial conditions tested: '+str(trials))
904 |     param.close()
905 |     os.chdir('../')
906 |     os.chdir('../')
907 | 
908 |     #Reckon
909 |     if not flag_train:  
910 |         output_reckon = ann_reckon(reckon,weights,peights,biasH,biasO)
911 |     
912 |         a = np.reshape(output_reckon,(col,row),order='F') 
913 |         a = np.transpose(a)
914 |     else:
915 |         a = 0
916 |     
917 |     return a


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
  1 |                     GNU GENERAL PUBLIC LICENSE
  2 |                        Version 3, 29 June 2007
  3 | 
  4 |  Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
  5 |  Everyone is permitted to copy and distribute verbatim copies
  6 |  of this license document, but changing it is not allowed.
  7 | 
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235 |   A compilation of a covered work with other separate and independent
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245 |   6. Conveying Non-Source Forms.
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247 |   You may convey a covered work in object code form under the terms
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275 |     d) Convey the object code by offering access from a designated
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288 |     e) Convey the object code using peer-to-peer transmission, provided
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290 |     Source of the work are being offered to the general public at no
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297 |   A "User Product" is either (1) a "consumer product", which means any
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318 |   If you convey an object code work under this section in, or with, or
319 | specifically for use in, a User Product, and the conveying occurs as
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338 | in accord with this section must be in a format that is publicly
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341 | unpacking, reading or copying.
342 | 
343 |   7. Additional Terms.
344 | 
345 |   "Additional permissions" are terms that supplement the terms of this
346 | License by making exceptions from one or more of its conditions.
347 | Additional permissions that are applicable to the entire Program shall
348 | be treated as though they were included in this License, to the extent
349 | that they are valid under applicable law.  If additional permissions
350 | apply only to part of the Program, that part may be used separately
351 | under those permissions, but the entire Program remains governed by
352 | this License without regard to the additional permissions.
353 | 
354 |   When you convey a copy of a covered work, you may at your option
355 | remove any additional permissions from that copy, or from any part of
356 | it.  (Additional permissions may be written to require their own
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361 |   Notwithstanding any other provision of this License, for material you
362 | add to a covered work, you may (if authorized by the copyright holders of
363 | that material) supplement the terms of this License with terms:
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365 |     a) Disclaiming warranty or limiting liability differently from the
366 |     terms of sections 15 and 16 of this License; or
367 | 
368 |     b) Requiring preservation of specified reasonable legal notices or
369 |     author attributions in that material or in the Appropriate Legal
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388 |   All other non-permissive additional terms are considered "further
389 | restrictions" within the meaning of section 10.  If the Program as you
390 | received it, or any part of it, contains a notice stating that it is
391 | governed by this License along with a term that is a further
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393 | a further restriction but permits relicensing or conveying under this
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398 |   If you add terms to a covered work in accord with this section, you
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403 |   Additional terms, permissive or non-permissive, may be stated in the
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405 | the above requirements apply either way.
406 | 
407 |   8. Termination.
408 | 
409 |   You may not propagate or modify a covered work except as expressly
410 | provided under this License.  Any attempt otherwise to propagate or
411 | modify it is void, and will automatically terminate your rights under
412 | this License (including any patent licenses granted under the third
413 | paragraph of section 11).
414 | 
415 |   However, if you cease all violation of this License, then your
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421 | 
422 |   Moreover, your license from a particular copyright holder is
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424 | violation by some reasonable means, this is the first time you have
425 | received notice of violation of this License (for any work) from that
426 | copyright holder, and you cure the violation prior to 30 days after
427 | your receipt of the notice.
428 | 
429 |   Termination of your rights under this section does not terminate the
430 | licenses of parties who have received copies or rights from you under
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432 | reinstated, you do not qualify to receive new licenses for the same
433 | material under section 10.
434 | 
435 |   9. Acceptance Not Required for Having Copies.
436 | 
437 |   You are not required to accept this License in order to receive or
438 | run a copy of the Program.  Ancillary propagation of a covered work
439 | occurring solely as a consequence of using peer-to-peer transmission
440 | to receive a copy likewise does not require acceptance.  However,
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442 | modify any covered work.  These actions infringe copyright if you do
443 | not accept this License.  Therefore, by modifying or propagating a
444 | covered work, you indicate your acceptance of this License to do so.
445 | 
446 |   10. Automatic Licensing of Downstream Recipients.
447 | 
448 |   Each time you convey a covered work, the recipient automatically
449 | receives a license from the original licensors, to run, modify and
450 | propagate that work, subject to this License.  You are not responsible
451 | for enforcing compliance by third parties with this License.
452 | 
453 |   An "entity transaction" is a transaction transferring control of an
454 | organization, or substantially all assets of one, or subdividing an
455 | organization, or merging organizations.  If propagation of a covered
456 | work results from an entity transaction, each party to that
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462 | 
463 |   You may not impose any further restrictions on the exercise of the
464 | rights granted or affirmed under this License.  For example, you may
465 | not impose a license fee, royalty, or other charge for exercise of
466 | rights granted under this License, and you may not initiate litigation
467 | (including a cross-claim or counterclaim in a lawsuit) alleging that
468 | any patent claim is infringed by making, using, selling, offering for
469 | sale, or importing the Program or any portion of it.
470 | 
471 |   11. Patents.
472 | 
473 |   A "contributor" is a copyright holder who authorizes use under this
474 | License of the Program or a work on which the Program is based.  The
475 | work thus licensed is called the contributor's "contributor version".
476 | 
477 |   A contributor's "essential patent claims" are all patent claims
478 | owned or controlled by the contributor, whether already acquired or
479 | hereafter acquired, that would be infringed by some manner, permitted
480 | by this License, of making, using, or selling its contributor version,
481 | but do not include claims that would be infringed only as a
482 | consequence of further modification of the contributor version.  For
483 | purposes of this definition, "control" includes the right to grant
484 | patent sublicenses in a manner consistent with the requirements of
485 | this License.
486 | 
487 |   Each contributor grants you a non-exclusive, worldwide, royalty-free
488 | patent license under the contributor's essential patent claims, to
489 | make, use, sell, offer for sale, import and otherwise run, modify and
490 | propagate the contents of its contributor version.
491 | 
492 |   In the following three paragraphs, a "patent license" is any express
493 | agreement or commitment, however denominated, not to enforce a patent
494 | (such as an express permission to practice a patent or covenant not to
495 | sue for patent infringement).  To "grant" such a patent license to a
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497 | patent against the party.
498 | 
499 |   If you convey a covered work, knowingly relying on a patent license,
500 | and the Corresponding Source of the work is not available for anyone
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503 | then you must either (1) cause the Corresponding Source to be so
504 | available, or (2) arrange to deprive yourself of the benefit of the
505 | patent license for this particular work, or (3) arrange, in a manner
506 | consistent with the requirements of this License, to extend the patent
507 | license to downstream recipients.  "Knowingly relying" means you have
508 | actual knowledge that, but for the patent license, your conveying the
509 | covered work in a country, or your recipient's use of the covered work
510 | in a country, would infringe one or more identifiable patents in that
511 | country that you have reason to believe are valid.
512 | 
513 |   If, pursuant to or in connection with a single transaction or
514 | arrangement, you convey, or propagate by procuring conveyance of, a
515 | covered work, and grant a patent license to some of the parties
516 | receiving the covered work authorizing them to use, propagate, modify
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518 | you grant is automatically extended to all recipients of the covered
519 | work and works based on it.
520 | 
521 |   A patent license is "discriminatory" if it does not include within
522 | the scope of its coverage, prohibits the exercise of, or is
523 | conditioned on the non-exercise of one or more of the rights that are
524 | specifically granted under this License.  You may not convey a covered
525 | work if you are a party to an arrangement with a third party that is
526 | in the business of distributing software, under which you make payment
527 | to the third party based on the extent of your activity of conveying
528 | the work, and under which the third party grants, to any of the
529 | parties who would receive the covered work from you, a discriminatory
530 | patent license (a) in connection with copies of the covered work
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533 | contain the covered work, unless you entered into that arrangement,
534 | or that patent license was granted, prior to 28 March 2007.
535 | 
536 |   Nothing in this License shall be construed as excluding or limiting
537 | any implied license or other defenses to infringement that may
538 | otherwise be available to you under applicable patent law.
539 | 
540 |   12. No Surrender of Others' Freedom.
541 | 
542 |   If conditions are imposed on you (whether by court order, agreement or
543 | otherwise) that contradict the conditions of this License, they do not
544 | excuse you from the conditions of this License.  If you cannot convey a
545 | covered work so as to satisfy simultaneously your obligations under this
546 | License and any other pertinent obligations, then as a consequence you may
547 | not convey it at all.  For example, if you agree to terms that obligate you
548 | to collect a royalty for further conveying from those to whom you convey
549 | the Program, the only way you could satisfy both those terms and this
550 | License would be to refrain entirely from conveying the Program.
551 | 
552 |   13. Use with the GNU Affero General Public License.
553 | 
554 |   Notwithstanding any other provision of this License, you have
555 | permission to link or combine any covered work with a work licensed
556 | under version 3 of the GNU Affero General Public License into a single
557 | combined work, and to convey the resulting work.  The terms of this
558 | License will continue to apply to the part which is the covered work,
559 | but the special requirements of the GNU Affero General Public License,
560 | section 13, concerning interaction through a network will apply to the
561 | combination as such.
562 | 
563 |   14. Revised Versions of this License.
564 | 
565 |   The Free Software Foundation may publish revised and/or new versions of
566 | the GNU General Public License from time to time.  Such new versions will
567 | be similar in spirit to the present version, but may differ in detail to
568 | address new problems or concerns.
569 | 
570 |   Each version is given a distinguishing version number.  If the
571 | Program specifies that a certain numbered version of the GNU General
572 | Public License "or any later version" applies to it, you have the
573 | option of following the terms and conditions either of that numbered
574 | version or of any later version published by the Free Software
575 | Foundation.  If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 | 
579 |   If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
581 | public statement of acceptance of a version permanently authorizes you
582 | to choose that version for the Program.
583 | 
584 |   Later license versions may give you additional or different
585 | permissions.  However, no additional obligations are imposed on any
586 | author or copyright holder as a result of your choosing to follow a
587 | later version.
588 | 
589 |   15. Disclaimer of Warranty.
590 | 
591 |   THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
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595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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597 | IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599 | 
600 |   16. Limitation of Liability.
601 | 
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610 | SUCH DAMAGES.
611 | 
612 |   17. Interpretation of Sections 15 and 16.
613 | 
614 |   If the disclaimer of warranty and limitation of liability provided
615 | above cannot be given local legal effect according to their terms,
616 | reviewing courts shall apply local law that most closely approximates
617 | an absolute waiver of all civil liability in connection with the
618 | Program, unless a warranty or assumption of liability accompanies a
619 | copy of the Program in return for a fee.
620 | 
621 |                      END OF TERMS AND CONDITIONS
622 | 
623 |             How to Apply These Terms to Your New Programs
624 | 
625 |   If you develop a new program, and you want it to be of the greatest
626 | possible use to the public, the best way to achieve this is to make it
627 | free software which everyone can redistribute and change under these terms.
628 | 
629 |   To do so, attach the following notices to the program.  It is safest
630 | to attach them to the start of each source file to most effectively
631 | state the exclusion of warranty; and each file should have at least
632 | the "copyright" line and a pointer to where the full notice is found.
633 | 
634 |     <one line to give the program's name and a brief idea of what it does.>
635 |     Copyright (C) <year>  <name of author>
636 | 
637 |     This program is free software: you can redistribute it and/or modify
638 |     it under the terms of the GNU General Public License as published by
639 |     the Free Software Foundation, either version 3 of the License, or
640 |     (at your option) any later version.
641 | 
642 |     This program is distributed in the hope that it will be useful,
643 |     but WITHOUT ANY WARRANTY; without even the implied warranty of
644 |     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
645 |     GNU General Public License for more details.
646 | 
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648 |     along with this program.  If not, see <https://www.gnu.org/licenses/>.
649 | 
650 | Also add information on how to contact you by electronic and paper mail.
651 | 
652 |   If the program does terminal interaction, make it output a short
653 | notice like this when it starts in an interactive mode:
654 | 
655 |     <program>  Copyright (C) <year>  <name of author>
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657 |     This is free software, and you are welcome to redistribute it
658 |     under certain conditions; type `show c' for details.
659 | 
660 | The hypothetical commands `show w' and `show c' should show the appropriate
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662 | might be different; for a GUI interface, you would use an "about box".
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669 |   The GNU General Public License does not permit incorporating your program
670 | into proprietary programs.  If your program is a subroutine library, you
671 | may consider it more useful to permit linking proprietary applications with
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674 | <https://www.gnu.org/licenses/why-not-lgpl.html>.
675 | 


--------------------------------------------------------------------------------
/r.landslide.html:
--------------------------------------------------------------------------------
 1 | <h2>DESCRIPTION</h2>
 2 | <p><em>r.landslide</em> allows the identification of landslide susceptible areas on the basis of Artificial Neural Networks (ANNs), environmental parameters and landslide databases.</p>
 3 | <h3>ANN parameters tab</h3>
 4 | <p>In this guide, the user must enter the parameters related to the ANN architecture. There is the option to produce the susceptibility map only with an ANN, or from the ANN that had the best performance within a set of networks, from batch mode option (To do this, the user must check the box 'Train a set of ANNs and select the best one [batch mode]').</p>
 5 | <p><strong>Single ANN mode</strong></p>
 6 | <p>To create the map of susceptibility from a single ANN architeture, the user <strong>should not</strong> check the box 'Train a set of ANNs and select the best one [batch mode]'. The parameters required to use this mode are:</p>
 7 | <p><strong>Number of hidden neurons: </strong>The number of neurons in the hidden layer must be an integer. It is recommended that the maximum number of neurons in the intermediate layer be defined based on the Netch-Nielsen equation (Netch-Nielsen, 1987), which ensures that the neural network is able to approximate any continuous function and that depends on the number of thematic input parameters used by the user: N<sub>H </sub>&le; 2N<sub>I</sub> + 1,&nbsp;where&nbsp;N<sub>H</sub> is the number of neurons in the intermediate layer and&nbsp;N<sub>I</sub> is the number of input parameters. A default value of 12 is set.</p>
 8 | <p><strong>Learning rate: </strong>Learning rate is a hyper-parameter that controls how much we are adjusting the weights of our network with respect the loss gradient.&nbsp;This paramter is generally chosen in the interval [0,1]. Lower learning rates may require a great number of training epochs. A default value of 0.6 is set.</p>
 9 | <p><strong>Number of epochs: </strong>An epoch is a measure of the number of times all of the training vectors are used to update the weights. A default value of 200 is set. In order to avoid overfitting due to a probable high number of epochs, the early stopping method was implemented.</p>
10 | <p><strong>Batch mode</strong></p>
11 | <p>Batch mode is activated when the 'Train a set of ANNs and select the best one [batch mode]' box <strong>is selected</strong>. In this mode, a set of ANNs is generated from the variation of the number of neurons in the hidden layer and the set of initial conditions of the synaptic weights. The network that presented the best performance is then used to produce the susceptibility map.</p>
12 | <p>To do this, the user must specify the parameters marked with [batch mode] in the tab, in addition to the learning rate and number of epochs parameters previously described.</p>
13 | <p><strong>Minimum number of hidden neurons: </strong>Minimum number of hidden neurons to be tested. It is recommended that this parameter is not less than 2.</p>
14 | <p><strong>Maximum number of hidden neurons: </strong>Maximum number of hidden neurons to be tested. As previously mentioned, it is recommended that this value does not exceed that proposed by the equation of Netch-Nielsen&nbsp;(Netch-Nielsen, 1987) in order to ensure that the neural network is able to approximate any continuous function. If this parameter is set to 0, the maximum number of neurons will be calculated automatically based on equation.</p>
15 | <p><strong>Number of initial conditions: </strong>Number of initial sets of synaptic weights that will be tested for each set of neurons in the hidden layer. Attention: too high values can result in a long processing time.</p>
16 | <p><strong>Optional parameters of the ANN parameters tab</strong></p>
17 | <p>Considering that the construction phase of an ANN comprises three main steps: training, validation and testing, the input database must be restructured into three sets to serve these processes. The module allows the user to define a percentage of data that will be selected for the validation step and for the test step (0-1); however, it is recommended that the default values of 0.15 be maintained to avoid losses in the learning process from ANN.</p>
18 | <h3>Training tab</h3>
19 | <p>In this tab, the raster format thematic input parameters and the landslides and non-landslides events location vector files must be inserted. In this tab, there is also the option to perform only the training, validation and test steps. In this case, the module does not perform the calculation of susceptibility maps. This allows different network structures to be tested before the final susceptibility of the study area is evaluated. To do this, the users have to check the box 'Perform ONLY the training, validation and test steps'.</p>
20 | <p>A description of the parameters is given below:</p>
21 | <p><strong>Thematic layers: </strong>Thematic layers refer to environmental parameters of the region study that may influence the occurrence of a landslide event. Some examples include: elevation, slope, aspect, topographic wetness index, soil type, soil use, among others. The user can add different layers, and can verify the influence of each one of them by the sensitivity analysis performed by the module.</p>
22 | <p>This information can be obtained from online databases for the region of interest and must be entered into the module in raster format.</p>
23 | <p><strong>Point vector with landslides locations: </strong>Here, the user must indicate the vector file containing the location, in point format, of places where there were landslides of the region of interest. It is from this georeferenced information and thematic layers that the module will create the database for ANN training and application. It is recommended that for good ANN learning, a considerable number of slip points should be entered (averaging &gt; 50, depending on the size of the area).</p>
24 | <p>This type of information can be obtained from online databases, scientific works, public organs, etc.</p>
25 | <p><strong>Point vector with non-landslides locations: </strong>The user must specify the location of examples of places where there was no occurrence of slides in point vector format. The generation of these localities is usually given in a random manner, and should not overlap the landslide regions. It is recommended to generate the same number of points present in the vector file of landslide points.</p>
26 | <p><strong>Text-file with X,Y coordinates of landslides location:</strong> This is an <strong>optional</strong> feature that allows the user to enter a text file containing the coordinates in the form X, Y that will be automatically converted to vector format, instead of entering the vector file of the location of landslides from the previous option. The user can also enter the values directly in the box that appears.</p>
27 | <p><strong>Name of output susceptibility map: </strong>If the training-only checkbox is not checked, you must enter the name of the output raster map for susceptibility.</p>
28 | <p><strong>Directory name to save files (it will be created): </strong>You must enter the path to create the directory that will save performance results, ANN architecture, and sensitivity assessment.<strong><br /></strong></p>
29 | <h3>Reckoning tab</h3>
30 | <p>The Reckoning tab should be used in the case where only the network training, validation and test steps were performed by checking the 'check box' in the Training tab specified above. To perform this process, it is necessary to check the 'Perform ONLY the reckoning' check box and insert the directory containing the ANN architecture parameters which were stored after the training. Also, the user must identify the name of the map of susceptibility that will be generated.</p>
31 | <h3>Optional tab</h3>
32 | <p>Here, the user can insert a raster layer to delimit a smaller region of interest for the generation of the susceptibility map. When this option is used, a temporary region is created from the active layer. This function is interesting in cases where the user wants to perform the evaluation of a smaller area, thus reducing time and computational demand.</p>
33 | <h3>Outputs</h3>
34 | <p>The module will generate a raster map representing the susceptibility to landslides of the area of interest. The raster will contain values ranging from 0 to 1, values close to 0 will represent areas of lesser susceptibility, while values closer to 1 will represent areas of greater susceptibility to these events.</p>
35 | <p>In the user-specified directory on the Training tab, different information about the selected ANN performance will be saved, which include: training error graph, validation and test, text files containing input information, ANN architecture, and performance, and graphs of the sensitivity evaluation of each of the parameters used.</p>
36 | <p>Note that the directory still includes text files called weights, peights, biasH and biasO, which will be used by the Reckoning tab. Therefore, it is recommended that the names of these files not be changed.</p>
37 | <h2>NOTE</h2>
38 | <p>After generating the module's susceptibility map, the user must load the raster map to the workspace via the <em>d.rast</em> module.</p>
39 | <h2>REFERENCE</h2>
40 | <p>R. Netch-Nielsen, Kolmogorov&rsquo;s mapping neural network existence theorem, First IEEE International Joint Conference on Neural Networks 26 (1987) 11&ndash;14.</p>
41 | <h2>SEE ALSO</h2>
42 | <p><em> <a href="http://grass.osgeo.org/programming7/">GRASS Programmer's Manual</a> </em></p>
43 | <h2>AUTHOR</h2>
44 | <p>Lucimara Bragagnolo</p>
45 | <p>Roberto Valmir da Silva</p>
46 | <p>Jos&eacute; Mario Vicensi Grzybowski</p>
47 | <p><em>Last changed: $Date: 2019-01-03 12:59:19 +0200 (Thu, 03 Jan 2019) $</em></p>


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/r.landslide.py:
--------------------------------------------------------------------------------
  1 | #!/usr/bin/env python

  2 | #############################################################

  3 | #

  4 | #  MODULE: r.landslide

  5 | #

  6 | #  AUTHOR(S): Lucimara Bragagnolo -------- lucimarabragagnolo@hotmail.com

  7 | #             Roberto Valmir da Silva --------- roberto.silva@uffs.edu.br

  8 | #             Jose Mario Vicensi Grzybowski - jose.grzybowski@uffs.edu.br
  9 | #

 10 | #  PURPOSE: Uses r.landslide for identification of areas 

 11 | #           susceptible to landslides

 12 | #

 13 | #  COPYRIGHT: (C) 2019 by the GRASS Development Team

 14 | #

 15 | #  This program is free software; you can redistribute it and/or modify

 16 | #  it under the terms of the GNU General Public License as published by

 17 | #  the Free Software Foundation; either version 2 of the License, or

 18 | #  (at your option) any later version.

 19 | #

 20 | #  This program is distributed in the hope that it will be useful,

 21 | #  but WITHOUT ANY WARRANTY; without even the implied warranty of

 22 | #  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

 23 | #  GNU General Public License for more details.

 24 | #############################################################

 25 | 
 26 | 
 27 | #%module: r.landslide
 28 | #% description: Creates a landslide susceptibility map
 29 | #% keyword: raster
 30 | #%end
 31 | 
 32 | #%option
 33 | #% key: num_hidden
 34 | #% type: integer
 35 | #% label: Number of hidden neurons
 36 | #% answer: 12
 37 | #% guisection: ANN parameters
 38 | #% required: no
 39 | #%end
 40 | 
 41 | #%option
 42 | #% key: lr_rate
 43 | #% type: double
 44 | #% label: Learning rate (0-1)
 45 | #% answer: 0.6
 46 | #% guisection: ANN parameters
 47 | #% required: no
 48 | #%end
 49 | 
 50 | #%option
 51 | #% key: nr_epochs
 52 | #% type: integer
 53 | #% label: Number of epochs
 54 | #% answer: 200
 55 | #% guisection: ANN parameters
 56 | #% required: no
 57 | #%end
 58 | 
 59 | #%option
 60 | #% key: val_samples
 61 | #% type: double
 62 | #% label: Percentage of data for validation (0-1)
 63 | #% answer: 0.15
 64 | #% guisection: ANN parameters
 65 | #% required: no
 66 | #%end
 67 | 
 68 | #%option
 69 | #% key: test_samples
 70 | #% type: double
 71 | #% label: Percentage of data for test (0-1)
 72 | #% answer: 0.15
 73 | #% guisection: ANN parameters
 74 | #% required: no
 75 | #%end
 76 | 
 77 | #%flag
 78 | #% key: b
 79 | #% label: Train a set of ANNs and select the best one [batch mode]
 80 | #% guisection: ANN parameters
 81 | #%end
 82 | 
 83 | #%option
 84 | #% key: min_hidden
 85 | #% type: integer
 86 | #% label: [batch mode] Minimum number of hidden neurons
 87 | #% answer: 2
 88 | #% guisection: ANN parameters
 89 | #% required: no
 90 | #%end
 91 | 
 92 | #%option
 93 | #% key: max_hidden
 94 | #% type: integer
 95 | #% label: [batch mode] Maximum number of hidden neurons
 96 | #% guisection: ANN parameters
 97 | #% answer: 0
 98 | #% required: no
 99 | #%end
100 | 
101 | #%option
102 | #% key: init_cond
103 | #% type: integer
104 | #% label: [batch mode] Number of initial conditions
105 | #% description: Number of times the synaptic weights are restarted
106 | #% guisection: ANN parameters
107 | #% answer: 1
108 | #% required: no
109 | #%end
110 | 
111 | #%flag
112 | #% key: s
113 | #% label: Perform ONLY the training, validation and test steps
114 | #% guisection: Training
115 | #%end
116 | 
117 | #%option G_OPT_R_INPUT
118 | #% key: layers
119 | #% multiple: yes
120 | #% label: Thematic layers
121 | #% description: Insert rasters of environmental parameters that influence the occurrence of landslides
122 | #% guisection: Training
123 | #% required: no
124 | #%end
125 | 
126 | #%option G_OPT_V_INPUT
127 | #% key: landslide
128 | #% label: Point vector with landslides locations
129 | #% description: Vector layer containing the location of landslides points
130 | #% required: no
131 | #% guisection: Training
132 | #%end
133 | 
134 | #%option G_OPT_V_INPUT
135 | #% key: nolandslide
136 | #% label: Point vector with non-landslides locations
137 | #% description: Vector layer containing the location of non-landslides points
138 | #% required: no
139 | #% guisection: Training
140 | #%end
141 | 
142 | #%option G_OPT_F_INPUT
143 | #% key: coord
144 | #% label: [optional] Text file with X,Y coordinates of landslides locations
145 | #% description: Text file containing the landslides points coordinates
146 | #% required: no
147 | #% guisection: Training
148 | #% required: no 
149 | #%end
150 | 
151 | #%option G_OPT_F_SEP
152 | #% key: separator
153 | #% label: Character separator of text file containing the X,Y coordinates
154 | #% description: Character separator of text file containing the X,Y coordinates
155 | #% required: no
156 | #% guisection: Training
157 | #% answer: comma
158 | #% required: no 
159 | #%end
160 | 
161 | #%option G_OPT_R_OUTPUT
162 | #% key: output
163 | #% description: Name of output susceptibility map
164 | #% guisection: Training
165 | #% required: no
166 | #%end
167 | 
168 | #%option G_OPT_F_OUTPUT
169 | #% key: directory
170 | #% label: Directory name to save files (it will be created)
171 | #% description: Directory name to save the ANN training results
172 | #% required: no
173 | #% guisection: Training
174 | #%end
175 | 
176 | #%flag
177 | #% key: r
178 | #% description: Perform ONLY the application
179 | #% guisection: Application
180 | #%end
181 | 
182 | #%option G_OPT_M_DIR
183 | #% key: direc_reckon
184 | #% description: Input path directory containing weights, peights and bias text files
185 | #% guisection: Application
186 | #% required: no
187 | #%end
188 | 
189 | #%option G_OPT_R_OUTPUT
190 | #% key: outputr
191 | #% description: Name of output susceptibility map
192 | #% guisection: Application
193 | #% required: no
194 | #%end
195 | 
196 | #%flag
197 | #% key: f
198 | #% description: Save susceptibility raster in directory
199 | #%end
200 | 
201 | #%option G_OPT_R_INPUT
202 | #% key: dregion
203 | #% label: Insert a rast map to define a temporary region to reckoning step
204 | #% required: no
205 | #%end
206 | 
207 | import sys
208 | import grass.script as gscript
209 | from grass.script import array as garray
210 | import numpy as np
211 | from ANN_module import ann_module 
212 | from ANN_module import ann_reckon
213 | from ANN_module import ANN_batch
214 | import os
215 | 
216 | def main():
217 |     options, flags = gscript.parser()
218 | 
219 |     #Output
220 |     raster_final = options['output']
221 | 
222 |     #Inputs
223 |     nr_epochs = int(options['nr_epochs'])
224 |     num_hidden = int(options['num_hidden'])
225 |     coef = float(options['lr_rate'])
226 |     val_samples = float(options['val_samples'])
227 |     test_samples = float(options['test_samples'])
228 |     min_hidden = int(options['min_hidden'])
229 |     max_hidden = int(options['max_hidden'])
230 |     trials = int(options['init_cond'])
231 |     directory = options['directory']
232 |     flag_batch = flags['b'] #To train a set of ANNs
233 |     flag_train = flags['s']
234 |     flag_reckon = flags['r']
235 |     flag_save = flags['f']
236 |     direc_reckon = options['direc_reckon']
237 |     coord = options['coord']
238 |     tregion = options['dregion']
239 |    
240 |     
241 |     #Parameters verification
242 |     if num_hidden == 0:
243 |         gscript.fatal(_("Zero is not a valid value for number of hidden neuros."))
244 |    
245 |     if nr_epochs == 0:
246 |         gscript.fatal(_("Zero is not a valid value for number of epochs."))
247 | 
248 |     if coef == 0:
249 |         gscript.fatal(_("Zero is not a valid value for learning rate."))
250 |     
251 |     if coef >= 1:
252 |         gscript.fatal(_("This is not a valid value for the learning rate. Please enter a value within the range 0-1."))
253 | 
254 |     if val_samples == 0:
255 |         gscript.fatal(_("Zero is not a valid value to create the validation dataset."))
256 | 
257 |     if val_samples >= 1:
258 |         gscript.fatal(_("This is not a valid value to create the validation dataset."))
259 | 
260 |     if trials == 0:
261 |         gscript.fatal(_("This is not a valid value for the number of initial conditions."))
262 | 
263 |     if flag_train and flag_reckon:
264 |         gscript.fatal(_("The both flags Perform ONLY reckoning and Perform ONLY training are selected. Please uncheck one."))
265 | 
266 |     if flag_batch and flag_reckon:
267 |         gscript.fatal(_("The both flags Train a set of ANNs and Perform ONLY reckoning are selected. Please uncheck one."))
268 | 
269 |     if test_samples == 0:
270 |         gscript.fatal(_("Zero is not a valid value to create the test dataset."))
271 | 
272 |     if val_samples >= 1:
273 |         gscript.fatal(_("This is not a valid value to create the test dataset."))
274 | 
275 |     if flag_batch:
276 |         if min_hidden == 0:
277 |             min_hidden = 2
278 |             gscript.warning(_("The minimum number of hidden neurons was defined as 2"))
279 | 
280 |         if max_hidden == 0:
281 |             gscript.warning(_("The maximum number of hidden neurons will be calculated based on the number of input parameters."))
282 | 
283 |     if val_samples > 0.20:
284 |         gscript.warning(_("A high percentage for validation data reduces the number of training records. Consider reducing the value of this parameter."))
285 | 
286 |     if test_samples > 0.20:
287 |         gscript.warning(_("A high percentage for test data reduces the number of training records. Consider reducing the value of this parameter."))
288 | 
289 | 
290 |     #print flag_reckon
291 |     work_dir = os.getcwd() #Get the current working directory
292 | 
293 |     if flag_reckon is False:
294 |         #Get the input map layers
295 |         rasters = options['layers'].split(",") #get the raster maps names (comma separated)
296 |         #split: it separates the maps names and creates a list
297 | 
298 |         n = len(rasters) #number of input maps
299 |         array_maps = []
300 | 
301 |         #Creates a temporary region for calculation
302 |         if tregion:
303 |             gscript.use_temp_region()
304 |             gscript.run_command('g.region', raster=tregion, align=rasters[0])
305 | 
306 |             #Create a matrix with the map layers for reckon with tregion
307 |             for i in range(0,n):
308 |                 a = garray.array(mapname=rasters[i],null=-9999) #this function does not aceppt NAN values
309 |                 a[a==-9999]=np.nan #replacing to NaN
310 |                 array_maps.append(a) #List with all maps 
311 | 
312 |             row = int(a.shape[0])
313 |             col = int(a.shape[1])
314 |             lines = int(row*col)
315 | 
316 |             reckon = np.zeros((lines,n))
317 |             for i in range(0,n):
318 |                 reckon[:,i] = np.asarray(array_maps[i].reshape((lines,)))
319 |             
320 |             gscript.del_temp_region()
321 | 
322 |         else:
323 |             #Create a matrix with the map layers for reckon without region
324 |             for i in range(0,n):
325 |                 a = garray.array(mapname=rasters[i],null=-9999) #this function does not aceppt NAN values
326 |                 a[a==-9999]=np.nan #replacing to NaN
327 |                 array_maps.append(a) #List with all maps 
328 | 
329 |             row = int(a.shape[0])
330 |             col = int(a.shape[1])
331 |             lines = int(row*col)
332 | 
333 |             reckon = np.zeros((lines,n))
334 |             for i in range(0,n):
335 |                 reckon[:,i] = np.asarray(array_maps[i].reshape((lines,)))
336 | 
337 |         if not coord:
338 |             landslide = options['landslide']
339 |         else:
340 |             separator = options['separator']
341 |             gscript.run_command(
342 |                 "v.in.ascii",
343 |                 input=coord,
344 |                 output='landslide_points',
345 |                 separator=separator,
346 |                 overwrite=True)
347 |             landslide = 'landslide_points'
348 | 
349 |         #Get the vector points layers
350 |         nolandslide = options['nolandslide']
351 | 
352 |         #Collect the information from the rasters (v.what.rast)
353 |         vecname = landslide,nolandslide
354 | 
355 |         columnst = []
356 |         for i in range(n):
357 |             r = rasters[i] #Get the raster name
358 |             c = r #Vector column name is the same that raster name
359 |             if c.endswith('@PERMANENT'):
360 |                 c = c[:-10]
361 |             columnst.append(c) #Vector columns title
362 |             for j in range(0,2): #vectors landslide/no-landslide
363 |                 v = vecname[j] #Landslides or nonlandslide
364 |                 gscript.run_command(
365 |                     "v.what.rast", 
366 |                     map=v,
367 |                     raster=r,
368 |                     column=c
369 |                     )   
370 | 
371 |         tempfile = gscript.tempfile() #Creates a temporary file
372 |         gscript.run_command(
373 |             "v.out.ascii",
374 |             input=landslide,
375 |             output=tempfile+'.txt',
376 |             columns=columnst,
377 |             format='point',
378 |             separator='comma'
379 |             )
380 | 
381 |         land = np.genfromtxt(tempfile+'.txt', delimiter=",") #This command permits open csv with missing values
382 |         land = np.delete(land,[0,1,2],axis=1) #Delete columns with coords and ID
383 | 
384 |         nanvalues = np.argwhere(np.isnan(land)) #Checking for nan values
385 |         
386 |         if nanvalues.size > 0:
387 |             k = 0
388 |             nanvalues = np.argwhere(np.isnan(land))
389 |             while nanvalues.size > 0:
390 |                 k = k + 1
391 |                 land = np.delete(land, (int(nanvalues[0,0])), axis=0) #Delete the line with nan
392 |                 nanvalues = np.argwhere(np.isnan(land))
393 | 
394 |             gscript.warning("Some rows with null values of landslide points were deleted.")
395 | 
396 |         land = np.append(land,np.ones((land.shape[0],1)),axis=1) #Add column of ones (landslides)
397 | 
398 |         tempfile2 = gscript.tempfile()
399 |         gscript.run_command(
400 |            "v.out.ascii",
401 |            input=nolandslide,
402 |            output=tempfile2+'.txt',
403 |            columns=columnst,
404 |            format='point',
405 |            separator='comma'
406 |             )
407 | 
408 |         noland = np.genfromtxt(tempfile2+'.txt', delimiter=",")
409 |         noland = np.delete(noland,[0,1,2],axis=1)
410 | 
411 |         nanvalues = np.argwhere(np.isnan(noland)) #Checking for nan values
412 | 
413 |         if nanvalues.size > 0:
414 |             k = 0
415 |             nanvalues = np.argwhere(np.isnan(noland))
416 |             while nanvalues.size > 0:
417 |                 k = k + 1
418 |                 noland = np.delete(noland, (int(nanvalues[0,0])), axis=0) #Delete the line with nan
419 |                 nanvalues = np.argwhere(np.isnan(noland))
420 | 
421 |             gscript.warning("Some rows with null values of nonlandslide points were deleted.")
422 |         
423 |         noland = np.append(noland,np.zeros((noland.shape[0],1)),axis=1) #Add column of zeros (non landslides)
424 | 
425 |         #Join tables
426 |         dataset = np.append(land,noland,axis=0)
427 |         np.random.shuffle(dataset) #Randomize dataset
428 |         
429 |         #Input and outuput data from training, validation and test
430 |         input_data = dataset[:,0:n]
431 |         output_data = dataset[:,n]
432 | 
433 |         if not flag_batch: #Just one neural network trained
434 |             final = ann_module(input_data,output_data,reckon,num_hidden,coef,nr_epochs,
435 |                 val_samples,test_samples,directory,columnst,col,row,flag_train)
436 | 
437 |         else: #Flag_batch is true
438 |             if max_hidden == 0:
439 |                 max_hidden = n*2+1
440 | 
441 |             hidden = np.arange(min_hidden,max_hidden+1,1)
442 |             
443 |             gscript.message(_("Definitions for batch mode: "))
444 |             gscript.message(_("Hidden neurons: "))
445 |             gscript.message(hidden)
446 |             gscript.message(_("Number of initial conditions: "))
447 |             gscript.message(trials)
448 | 
449 |             total_ANNs = len(hidden)*trials
450 |             gscript.message(_("Number of ANNs that will be tested: "))
451 |             gscript.message(total_ANNs)
452 |             gscript.warning(_("This may take a while!"))
453 |     
454 |             final = ANN_batch(input_data,output_data,reckon,hidden,trials,coef,
455 |                 nr_epochs,val_samples,test_samples,directory,columnst,col,row,flag_train)
456 | 
457 |         #Save rasters name in order
458 |         os.chdir(directory)
459 |         with open("rasters_names.txt", "w") as my_file:
460 |             my_file.write(options['layers'])
461 |         os.chdir(work_dir)
462 | 
463 |         if not flag_train: #Generates final map (is not just training)
464 | 
465 |             #In case there is temp region
466 |             if tregion:
467 |                 gscript.use_temp_region()
468 |                 gscript.run_command('g.region', raster=tregion, align=rasters[0])
469 |                 
470 |                 suscep = garray.array()
471 |                 for i in range(0,final.shape[0]):
472 |                     for j in range(0,final.shape[1]):
473 |                         suscep[i,j] = final[i,j]
474 | 
475 |                 suscep.write(mapname=raster_final, overwrite=True) #Create raster file
476 | 
477 |                 if flag_save: #Save final raster in the directory
478 |                     os.chdir(directory)
479 |                     gscript.run_command(
480 |                         "r.out.gdal",
481 |                         input=raster_final,
482 |                         output=str(raster_final)+'.tiff',
483 |                         format='GTiff',
484 |                         type='Float64'
485 |                         )
486 |                     os.chdir(work_dir)
487 |                 gscript.del_temp_region()
488 | 
489 |             else:
490 |                 suscep = garray.array()
491 |                 for i in range(0,final.shape[0]):
492 |                     for j in range(0,final.shape[1]):
493 |                         suscep[i,j] = final[i,j]
494 | 
495 |                 suscep.write(mapname=raster_final, overwrite=True) #Create raster file
496 | 
497 |                 if flag_save: #Save final raster in the directory
498 |                     os.chdir(directory)
499 |                     gscript.run_command(
500 |                         "r.out.gdal",
501 |                         input=raster_final,
502 |                         output=str(raster_final)+'.tiff',
503 |                         format='GTiff',
504 |                         type='Float64'
505 |                         )
506 |                     os.chdir(work_dir)
507 | 
508 |     else: #flag_reckon is True
509 | 
510 |         raster_final = options['outputr']
511 | 
512 |         os.chdir(direc_reckon)
513 |         weights = np.genfromtxt('weights.txt', delimiter=",") 
514 |         peights = np.genfromtxt('peights.txt', delimiter=",") 
515 |         peights = np.reshape(peights,(peights.shape[0],1))
516 |         biasH = np.genfromtxt('biasH.txt', delimiter=",") 
517 |         biasH = np.reshape(biasH,(biasH.shape[0],1))
518 |         biasO = np.genfromtxt('biasO.txt', delimiter=",") 
519 |         biasO = np.reshape(biasO,(1,1))
520 |         max_in = np.genfromtxt('max_in.txt', delimiter=",") 
521 |         max_in = np.reshape(max_in,(1,max_in.shape[0]))
522 |         min_in = np.genfromtxt('min_in.txt', delimiter=",") 
523 |         min_in = np.reshape(min_in,(1,min_in.shape[0]))
524 | 
525 |         with open('rasters_names.txt', 'r') as myfile:
526 |           rasters = myfile.read()
527 |         os.chdir(work_dir)
528 | 
529 |         #Get the input map layers
530 |         rasters = rasters.split(",") #get the raster maps names (comma separated)
531 |         #split: it separates the maps names and creates a list
532 |         n = len(rasters) #number of input maps
533 |         array_maps = []
534 | 
535 |         #Creates a temporary region for calculation
536 |         if tregion:
537 |             gscript.use_temp_region()
538 |             gscript.run_command('g.region', raster=tregion, align=rasters[0])
539 | 
540 |             #Create a matrix with the map layers for reckon with tregion
541 |             for i in range(0,n):
542 |                 a = garray.array(mapname=rasters[i],null=-9999) #this function does not aceppt NAN values
543 |                 a[a==-9999]=np.nan #replacing to NaN
544 |                 array_maps.append(a) #List with all maps 
545 | 
546 |             row = int(a.shape[0])
547 |             col = int(a.shape[1])
548 |             lines = int(row*col)
549 | 
550 |             reckon = np.zeros((lines,n))
551 |             for i in range(0,n):
552 |                 reckon[:,i] = np.asarray(array_maps[i].reshape((lines,)))
553 |             
554 |             gscript.del_temp_region()
555 | 
556 |         else:
557 |             #Create a matrix with the map layers for reckon without region
558 |             for i in range(0,n):
559 |                 a = garray.array(mapname=rasters[i],null=-9999) #this function does not aceppt NAN values
560 |                 a[a==-9999]=np.nan #replacing to NaN
561 |                 array_maps.append(a) #List with all maps 
562 | 
563 |             row = int(a.shape[0])
564 |             col = int(a.shape[1])
565 |             lines = int(row*col)
566 | 
567 |             reckon = np.zeros((lines,n))
568 |             for i in range(0,n):
569 |                 reckon[:,i] = np.asarray(array_maps[i].reshape((lines,)))
570 | 
571 |         for j in range(0,reckon.shape[1]): 
572 |             if max_in[0,j] != 0:
573 |                 reckon[:,j] = (reckon[:,j] - min_in[0,j])/(max_in[0,j]-min_in[0,j])
574 |         
575 |         output_reckon = ann_reckon(reckon,weights,peights,biasH,biasO)
576 | 
577 |         final = np.reshape(output_reckon,(col,row),order='F')
578 |         final = np.transpose(final)
579 | 
580 |         #In case there is temp region
581 |         if tregion:
582 |             gscript.use_temp_region()
583 |             gscript.run_command('g.region', raster=tregion, align=rasters[0])
584 |             
585 |             suscep = garray.array()
586 |             for i in range(0,final.shape[0]):
587 |                 for j in range(0,final.shape[1]):
588 |                     suscep[i,j] = final[i,j]
589 | 
590 |             suscep.write(mapname=raster_final, overwrite=True) #Create raster file
591 | 
592 |             if flag_save: #Save final raster in the directory
593 |                 os.chdir(directory)
594 |                 gscript.run_command(
595 |                     "r.out.gdal",
596 |                     input=raster_final,
597 |                     output=str(raster_final)+'.tiff',
598 |                     format='GTiff',
599 |                     type='Float64'
600 |                     )
601 |                 os.chdir(work_dir)
602 |             gscript.del_temp_region()
603 | 
604 |         else:
605 |             suscep = garray.array()
606 |             for i in range(0,final.shape[0]):
607 |                 for j in range(0,final.shape[1]):
608 |                     suscep[i,j] = final[i,j]
609 | 
610 |             suscep.write(mapname=raster_final, overwrite=True) #Create raster file
611 | 
612 |             if flag_save: #Save final raster in the directory
613 |                 os.chdir(directory)
614 |                 gscript.run_command(
615 |                     "r.out.gdal",
616 |                     input=raster_final,
617 |                     output=str(raster_final)+'.tiff',
618 |                     format='GTiff',
619 |                     type='Float64'
620 |                     )
621 |                 os.chdir(work_dir)
622 | 
623 |     return 0
624 | #Do not use the print statement (print function in Python 3) for informational output. This is reserved for standard module output if it has one. 
625 | #https://trac.osgeo.org/grass/wiki/Submitting/Python
626 | 
627 | if __name__ == "__main__":
628 |     sys.exit(main())
629 | 


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