evaluations(ty, pv) -> (ACC, MSE, SCC) Calculate accuracy, mean squared error and squared correlation coefficient using the true values (ty) and

Linear map: output[k] = sum_i(Matrix[k, i] * args[i] ) + Bias[k] Args: args: a tensor or a list of 2D, batch x n, Tensors. output_size: i

read in a problem (in svmlight format)

label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data perm, length l, must be allocated before ca

svm_read_problem(data_file_name) -> [y, x] Read LIBSVM-format data from data_file_name and return labels y and data instances x.

svm_save_model(model_file_name, model) -> None Save a LIBSVM model to the file model_file_name.

toPyModel(model_ptr) -> svm_model Convert a ctypes POINTER(svm_model) to a Python svm_model

this method is different from the the method self.train() we use the training data, do some feature selection, then train, ge

Maps sentencs and labels to vectors based on a vocabulary.

Builds a vocabulary mapping from word to index based on the sentences. Returns vocabulary mapping and inverse vocabulary mapping.

according to the input data, calculate the probability of the each class :param input_data: