def callSuitable(mlalgo, v2):
"""use and evaluate the selected Machine Learning algorithm"""
global X, Y, XT, YT
if mlalgo == 'Decision Tree':
model = models.DTModel(X, Y, XT, YT, v2)
model.start()
elif mlalgo == 'Naive Bayes':
model = models.NBModel(X, Y, XT, YT, v2)
model.start()
elif mlalgo == 'SVM':
model = models.SVMModel(X, Y, XT, YT, v2)
model.start()
else:
model = models.KNNModel(X, Y, XT, YT, v2)
model.start()
def callSuitable(mlalgo, v2):
#evaluate the selected Machine Learning algorithm
global X, Y, XT, YT
if mlalgo == 'Decision Tree':
model = models.DTModel(X, Y, XT, YT, v2)
model.start()
elif mlalgo == 'Naive Bayes':
model = models.NBModel(X, Y, XT, YT, v2)
model.start()
elif mlalgo == 'SVM':
model = models.SVMModel(X, Y, XT, YT, v2)
model.start()
elif mlalgo == 'K Nearest Neighbours':
model = models.KNNModel(X, Y, XT, YT, v2)
model.start()
elif mlalgo == 'Logistic Regression':
model = models.LogModel(X, Y, XT, YT, v2)
model.start()
else:
model = models.ANNModel(X, Y, XT, YT, v2)
model.start()
def loadModel(modelName, fileName=None):
"""load the modelName ML model and test the accuracy"""
global X, Y, XT, YT
mlalgo = modelName
if mlalgo == 'Decision Tree':
model = models.DTModel(X, Y, XT, YT)
model.start()
elif mlalgo == 'Naive Bayes':
model = models.NBModel(X, Y, XT, YT)
model.start()
elif mlalgo == 'SVM':
model = models.SVMModel(X, Y, XT, YT)
model.start()
elif mlalgo == 'K Nearest Neighbours':
model = models.KNNModel(X, Y, XT, YT)
model.start()
elif mlalgo == 'Logistic Regression':
model = models.LogModel(X, Y, XT, YT)
model.start()
else:
model = models.ANNModel(X, Y, XT, YT)
model.start()
reduced_features.append(feature_vector)
reduced_labels.append(label)
class_counts[label] += 1
reduced_features = torch.stack(reduced_features)
return reduced_features, reduced_labels
@staticmethod
def _reduce_random(features: torch.Tensor, labels, reduction=0.2):
"""
Sample the features at random.
:param features: Training features.
:param labels: Training labels.
:param reduction: Proportion to reduce to.
:return: Reduced features and labels.
"""
num_features = features.shape[0]
idxs = np.random.randint(low=0,
high=num_features,
size=int(reduction * num_features))
reduced_features = features[idxs]
reduced_labels = labels[idxs]
return reduced_features, reduced_labels
if __name__ == "__main__":
print("Creating LDA model")
svm_model = models.SVMModel("svm_alexnet256_poly8")
print("Creating feature extractor")
trainer = FeatureTrainer(AlexNet256())
trainer.train(svm_model)