def fit(self, X, y):
"""
@param X : an n-by-m array-like object containing n examples with m
features
@param y : an array-like object of length n containing -1/+1 labels
"""
self._X = np.asmatrix(X)
self._y = np.asmatrix(y).reshape((-1, 1))
if self.scale_C:
C = self.C / float(len(self._X))
else:
C = self.C
K, H, f, A, b, lb, ub = self._setup_svm(self._X, self._y, C)
# Solve QP
self._alphas, self._objective = quadprog(H, f, A, b, lb, ub,
self.verbose)
self._compute_separator(K)
def fit(self, bags, y):
"""
@param bags : a sequence of n bags; each bag is an m-by-k array-like
object containing m instances with k features
@param y : an array-like object of length n containing -1/+1 labels
"""
bs = BagSplitter([elem for elem in map(np.asmatrix, bags)],
np.asmatrix(y).reshape((-1, 1)))
self._bags = bs.neg_inst_as_bags + bs.pos_bags
self._y = np.matrix(
np.vstack([-np.ones((bs.L_n, 1)),
np.ones((bs.X_p, 1))]))
if self.scale_C:
iC = float(self.C) / bs.L_n
bC = float(self.C) / bs.X_p
else:
iC = self.C
bC = self.C
C = np.vstack([iC * np.ones((bs.L_n, 1)), bC * np.ones((bs.X_p, 1))])
if self.verbose:
print('Setup QP...')
K, H, f, A, b, lb, ub = self._setup_svm(self._bags, self._y, C)
# Adjust f with balancing terms
factors = np.vstack([
np.matrix(np.ones((bs.L_n, 1))),
np.matrix([2.0 / len(bag) - 1.0 for bag in bs.pos_bags]).T
])
f = np.multiply(f, factors)
if self.verbose:
print('Solving QP...')
self._alphas, self._objective = quadprog(H, f, A, b, lb, ub,
self.verbose)
self._compute_separator(K)
# Recompute predictions for full bags
self._bag_predictions = self.predict(bags)
def fit(self, bags, y):
"""
@param bags : a sequence of n bags; each bag is an m-by-k array-like
object containing m instances with k features
@param y : an array-like object of length n containing -1/+1 labels
"""
bs = BagSplitter(map(np.asmatrix, bags),
np.asmatrix(y).reshape((-1, 1)))
self._bags = bs.neg_inst_as_bags + bs.pos_bags
self._y = np.matrix(np.vstack([-np.ones((bs.L_n, 1)),
np.ones((bs.X_p, 1))]))
if self.scale_C:
iC = float(self.C) / bs.L_n
bC = float(self.C) / bs.X_p
else:
iC = self.C
bC = self.C
C = np.vstack([iC * np.ones((bs.L_n, 1)),
bC * np.ones((bs.X_p, 1))])
if self.verbose:
print('Setup QP...')
K, H, f, A, b, lb, ub = self._setup_svm(self._bags, self._y, C)
# Adjust f with balancing terms
factors = np.vstack([np.matrix(np.ones((bs.L_n, 1))),
np.matrix([2.0 / len(bag) - 1.0
for bag in bs.pos_bags]).T])
f = np.multiply(f, factors)
if self.verbose:
print('Solving QP...')
self._alphas, self._objective = quadprog(H, f, A, b, lb, ub,
self.verbose)
self._compute_separator(K)
# Recompute predictions for full bags
self._bag_predictions = self.predict(bags)
def fit(self, bags, y):
"""
@param bags : a sequence of n bags; each bag is an m-by-k array-like
object containing m instances with k features
@param y : an array-like object of length n containing -1/+1 labels
"""
self._bags = map(np.asmatrix, bags)
self._y = np.asmatrix(y).reshape((-1, 1))
if self.scale_C:
C = self.C / float(len(self._bags))
else:
C = self.C
if self.verbose:
print('Setup QP...')
K, H, f, A, b, lb, ub = self._setup_svm(self._bags, self._y, C)
# Solve QP
if self.verbose:
print('Solving QP...')
self._alphas, self._objective = quadprog(H, f, A, b, lb, ub,
self.verbose)
self._compute_separator(K)
def fit(self, bags, y):
"""
@param bags : a sequence of n bags; each bag is an m-by-k array-like
object containing m instances with k features
@param y : an array-like object of length n containing -1/+1 labels
"""
self._bags = list(map(np.asmatrix, bags))
self._y = np.asmatrix(y).reshape((-1, 1))
if self.scale_C:
C = self.C / float(len(self._bags))
else:
C = self.C
if self.verbose:
print('Setup QP...')
K, H, f, A, b, lb, ub = self._setup_svm(self._bags, self._y, C)
# Solve QP
if self.verbose:
print('Solving QP...')
self._alphas, self._objective = quadprog(H, f, A, b, lb, ub,
self.verbose)
self._compute_separator(K)