def fit(self, X, normalise=True):
"""
Construct the random projection forest for points in X.
Arguments:
- np.float64 array X [n_points, dim]
- optional boolean normalise: whether to normalise X. If True,
a copy of X will be made and
normalised.
Returns:
- object self
"""
if X.shape[0] < 1 or X.shape[1] < 1:
raise Exception('You must supply a valid 2D array.')
self.dim = X.shape[1]
if normalise:
self._X = X / np.linalg.norm(X, axis=1)[:, np.newaxis]
else:
self._X = X
# Reset the trees list in case of repeated calls to fit
self.trees = []
for _ in range(self.no_trees):
tree = Tree(self.leaf_size, self.dim)
tree.make_tree(self._X)
self.trees.append(tree)
return self
def fit(self, X, normalise=True):
"""
Construct the random projection forest for points in X.
Arguments:
- np.float64 array X [n_points, dim]
- optional boolean normalise: whether to normalise X. If True,
a copy of X will be made and
normalised.
Returns:
- object self
"""
if X.shape[0] < 1 or X.shape[1] < 1:
raise Exception("You must supply a valid 2D array.")
self.dim = X.shape[1]
if normalise:
self._X = X / np.linalg.norm(X, axis=1)[:, np.newaxis]
else:
self._X = X
# Reset the trees list in case of repeated calls to fit
self.trees = []
for _ in range(self.no_trees):
tree = Tree(self.leaf_size, self.dim)
tree.make_tree(self._X)
self.trees.append(tree)
return self
def fit(self, X, normalise=True):
"""
Construct the random projection forest for points in X.
Arguments:
- np.float64 array X [n_points, dim]
- optional boolean normalise: whether to normalise X. If True,
a copy of X will be made and
normalised.
"""
if self._is_constructed():
raise Exception('Tree has already been created.')
if X.shape[0] < 1 or X.shape[1] < 1:
raise Exception('You must supply a valid 2D array.')
self.dim = X.shape[1]
if normalise:
self._X = X / np.linalg.norm(X, axis=1)[:, np.newaxis]
else:
self._X = X
for _ in range(self.no_trees):
tree = Tree(self.leaf_size, self.dim)
tree.make_tree(self._X)
self.trees.append(tree)
