def fit(self, X, y):
self.trees = []
for m in range(self.num_trees):
print("Fitting tree %02d/%d..." % (m+1,self.num_trees))
tree = RandomTree(max_depth = self.max_depth)
tree.fit(X,y)
self.trees.append(tree)
def fit(self, X, y):
N, D = X.shape
trees = [None] * self.num_trees
for i in range(self.num_trees):
model = RandomTree(max_depth=self.max_depth)
model.fit(X, y)
trees[i] = model
self.trees = trees
def fit(self, X, y):
# Fit each tree
treeList = []
for i in range(self.num_trees):
tree = RandomTree(self.max_depth)
tree.fit(X, y)
treeList.append(tree)
self.treeList = treeList
def fit(self, X, y):
self.random_trees = []
for i in range(self.num_trees):
random_tree = RandomTree(max_depth=self.max_depth)
random_tree.fit(X, y)
self.random_trees.append(random_tree)
def fit(self, X, y):
# Train data on each tree
# initialize a forest
self.random_forest = []
for tree in range(self.num_trees):
# Bootstrapping and Random Trees Step
one_tree = RandomTree(max_depth=self.max_depth)
one_tree.fit(X, y)
self.random_forest.append(one_tree)
def fit(self, X, y):
listOfModels = []
for i in range(0, self.num_trees):
model = RandomTree(max_depth=np.inf)
model.fit(X, y)
listOfModels.append(model)
self.LOM = listOfModels
self.y_length = y.shape[0]
def fit(self, X, y):
self.trees = []
num_trees = self.num_trees
max_depth = self.max_depth
for m in range(num_trees):
tree = RandomTree(max_depth=max_depth)
tree.fit(X, y)
self.trees.append(tree)
def fit(self, X, y):
numTrees = self.num_trees
list_of_trees = []
for x in range(numTrees):
tree = RandomTree(max_depth=self.max_depth)
tree.fit(X, y)
list_of_trees.append(tree)
self.list_of_trees = list_of_trees
def fit(self, X, y):
forest = []
for n in range(self.num_trees):
model = RandomTree( max_depth = self.max_depth)
model.fit(X,y)
forest.append(model)
self.forest = forest
def fit(self, X, y):
rt = []
M = self.num_trees
for n in range(M):
#rt.append(RandomTree(self.max_depth))
rt.append(RandomTree.fit(self, X, y))
self.rt = rt
def fit(self, X, Y):
n, d = X.shape
self.trees = []
for i in range(self.n_trees):
idx = np.arange(n)
np.random.seed(np.int(time() / 150))
np.random.shuffle(idx)
X = X[idx]
Y = Y[idx]
train = np.int(self.ratio_per_tree * n)
Xtrain = X[:train, :]
Ytrain = Y[:train]
clf = RandomTree(max_depth=self.max_depth,
ratio_features=self.ratio_features)
clf.fit(Xtrain, Ytrain)
self.trees.append(clf)
def fit(self, X, y):
self.Random_Forest = []
for x in range(0, self.num_trees):
New_tree = RandomTree(self.max_depth)
New_tree.fit(X, y)
self.Random_Forest.append(New_tree)
def fit(self, X, y):
for i in range(self.num_trees):
model = RandomTree(max_depth=self.max_depth)
model.fit(X, y)
self.models.append(model)
