Training, crossvalidation and testing structural domain dataset
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... | @@ -208,12 +208,12 @@ if __name__ == "__main__": | ... | @@ -208,12 +208,12 @@ if __name__ == "__main__": |
208 | print(" Done!") | 208 | print(" Done!") |
209 | 209 | ||
210 | print(" Number of training classes: {}".format(len(y_train))) | 210 | print(" Number of training classes: {}".format(len(y_train))) |
211 | - print(" Number of training class RI: {}".format(y_train.count('DOM'))) | 211 | + print(" Number of training class DOM: {}".format(y_train.count('DOM'))) |
212 | print(" Number of training class OTHER: {}".format(y_train.count('OTHER'))) | 212 | print(" Number of training class OTHER: {}".format(y_train.count('OTHER'))) |
213 | print(" Shape of training matrix: {}".format(X_train.shape)) | 213 | print(" Shape of training matrix: {}".format(X_train.shape)) |
214 | 214 | ||
215 | print(" Number of testing classes: {}".format(len(y_test))) | 215 | print(" Number of testing classes: {}".format(len(y_test))) |
216 | - print(" Number of testing class RI: {}".format(y_test.count('DOM'))) | 216 | + print(" Number of testing class DOM: {}".format(y_test.count('DOM'))) |
217 | print(" Number of testing class OTHER: {}".format(y_test.count('OTHER'))) | 217 | print(" Number of testing class OTHER: {}".format(y_test.count('OTHER'))) |
218 | print(" Shape of testing matrix: {}".format(X_test.shape)) | 218 | print(" Shape of testing matrix: {}".format(X_test.shape)) |
219 | 219 | ||
... | @@ -276,13 +276,13 @@ if __name__ == "__main__": | ... | @@ -276,13 +276,13 @@ if __name__ == "__main__": |
276 | print(" Done!") | 276 | print(" Done!") |
277 | 277 | ||
278 | print("Training...") | 278 | print("Training...") |
279 | - classifier.fit(X_train, y_train) | 279 | + myClassifier.fit(X_train, y_train) |
280 | print(" Done!") | 280 | print(" Done!") |
281 | 281 | ||
282 | print("Testing (prediction in new data)...") | 282 | print("Testing (prediction in new data)...") |
283 | if args.reduction is not None: | 283 | if args.reduction is not None: |
284 | X_test = reduc.transform(X_test) | 284 | X_test = reduc.transform(X_test) |
285 | - y_pred = classifier.predict(X_test) | 285 | + y_pred = myClassifier.predict(X_test) |
286 | print(" Done!") | 286 | print(" Done!") |
287 | 287 | ||
288 | print("Saving report...") | 288 | print("Saving report...") | ... | ... |
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