def train(self, data, labels):
num_features = len(data[0])
k = math.floor(len(labels)/float(self.num))
p = math.ceil(np.sqrt(num_features))
for i in range(self.num):
indices_to_delete = np.random.permutation(num_features)[p:]
tree = DTree(self.depth, self.impurity, self.segmentor)
tree.train(np.delete(data[int(i*k):int((i+1)*k)], [], 1), labels[int(i*k):int((i+1)*k)])
if not isinstance(tree.root, LeafNode):
pass
self.trees.append(tree)
def train(self, data, labels):
def sample(data, labels):
n = data.shape[0]
samples = self.samples if self.samples else (n / 2)
i = np.random.choice(n, samples, replace=False)
return data[i], labels[i]
for index in range(self.nTrees):
tree = DTree(self.impurity,
self.segmentor,
self.depth,
self.randomness,
name=index)
sData, sLabel = sample(data, labels)
tree.train(sData, sLabel)
self.forest.append(tree)
def create_trees(bags, data, label):
N = len(bags)
trees = []
for k in range(N):
#b = pd.DataFrame(bags[k])
#print b.columns
t = DTree.get_tree(bags[k], label)
trees.append(t)
return trees
print('\t1. Drvo odlucivanja')
print('\t2. K najblizih suseda')
print('\t3. Gausova raspodela')
print('\t4. Neuronske mreze')
while True:
metoda = int(input(''))
if metoda in (1, 2, 3, 4):
break
else:
print('Neispravna opcija')
print('Odabrali ste metodu pod rednim brojem ' + str(metoda))
print('--------------------------------------------------------')
if metoda == 1:
print('Odabrali ste analizu metodom drveta odlučivanja')
DTree.DTree(data)
elif metoda == 2:
print('Odabrali ste analizu metodom k najbližih suseda')
KNeighbors.KNeighboors(data)
elif metoda == 3:
print('Odabrali ste analizu Gausovom metodom')
Gaussian.Gaussian(data)
elif metoda == 4:
print('Odabrali ste analizu metodom neuronskih mreža')
MLP.MLP(data)
else:
print('Neispravna opcija')
metoda = input('Zelite li da isprobate drugu metodu? (y/n)')
if metoda == 'n':
break
X = data['training_data'] Y = data['training_labels'].T.ravel() randomIndex = np.random.choice(TRAIN_SIZE, TRAIN_SIZE, replace=False) # Split Data xTrain = X[randomIndex[:-VALIDATION_SIZE]] yTrain = Y[randomIndex[:-VALIDATION_SIZE]] xValidate = X[randomIndex[-VALIDATION_SIZE:]] yValidate = Y[randomIndex[-VALIDATION_SIZE:]] xTest = data['test_data'] segmentor = Segmentor() print "============= Decision Tree ==========" tree = DTree(Impurity.impurity, segmentor, depth=20) tree.train(xTrain, yTrain) labels = tree.predict(xValidate) counts = np.bincount(tree.predict(xTrain) == yTrain) error = 1.0 - (counts[True] / float(counts[True] + counts[False])) print "Training Error: %f" % (error) counts = np.bincount(labels == yValidate) error = 1.0 - (counts[True] / float(counts[True] + counts[False])) print "Validation Error: %f" % (error) #import pdb; pdb.set_trace() print "========== Random Forest ==========" forest = RForest(Impurity.impurity, segmentor, nTrees=30, randomness=10)
