def __init__()

in liblinear/liblinear.py [0:0]

• 43 lines of code
• 7 McCabe index (conditional complexity)

	def __init__(self, y, x, bias = -1):
		if (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, scipy.ndarray))):
			raise TypeError("type of y: {0} is not supported!".format(type(y)))

		if isinstance(x, (list, tuple)):
			if len(y) != len(x):
				raise ValueError("len(y) != len(x)")
		elif scipy != None and isinstance(x, (scipy.ndarray, sparse.spmatrix)):
			if len(y) != x.shape[0]:
				raise ValueError("len(y) != len(x)")
			if isinstance(x, scipy.ndarray):
				x = scipy.ascontiguousarray(x) # enforce row-major
			if isinstance(x, sparse.spmatrix):
				x = x.tocsr()
				pass
		else:
			raise TypeError("type of x: {0} is not supported!".format(type(x)))
		self.l = l = len(y)
		self.bias = -1

		max_idx = 0
		x_space = self.x_space = []
		if scipy != None and isinstance(x, sparse.csr_matrix):
			csr_to_problem(x, self)
			max_idx = x.shape[1]
		else:
			for i, xi in enumerate(x):
				tmp_xi, tmp_idx = gen_feature_nodearray(xi)
				x_space += [tmp_xi]
				max_idx = max(max_idx, tmp_idx)
		self.n = max_idx

		self.y = (c_double * l)()
		if scipy != None and isinstance(y, scipy.ndarray):
			scipy.ctypeslib.as_array(self.y, (self.l,))[:] = y
		else:
			for i, yi in enumerate(y): self.y[i] = yi

		self.x = (POINTER(feature_node) * l)()
		if scipy != None and isinstance(x, sparse.csr_matrix):
			base = addressof(self.x_space.ctypes.data_as(POINTER(feature_node))[0])
			x_ptr = cast(self.x, POINTER(c_uint64))
			x_ptr = scipy.ctypeslib.as_array(x_ptr,(self.l,))
			x_ptr[:] = self.rowptr[:-1]*sizeof(feature_node)+base
		else:
			for i, xi in enumerate(self.x_space): self.x[i] = xi

		self.set_bias(bias)