import numpy as np
from data.loader_ac import DataLoader
import libsvm.svmutil as svm


def train_save():
    loader = DataLoader()
    data = [{i + 1: line[i] for i in range(len(line))} for line in loader.have]
    tags = np.ones(len(data)).tolist()
    pro = svm.svm_problem(tags, data)
    pra = svm.svm_parameter("-s 2 -g 2.57e-6 -n 7.1e-3")

    model = svm.svm_train(pro, pra)
    svm.svm_save_model("m2c/real.model", model)

    # predict test
    data = [{i + 1: line[i] for i in range(len(line))} for line in loader.test]
    tags = np.ones(len(data)).tolist()
    svm.svm_predict(y=tags, x=data, m=model)

    # predict none
    data = [{i + 1: line[i] for i in range(len(line))} for line in loader.none]
    tags = (np.ones(len(data)) * -1).tolist()
    svm.svm_predict(y=tags, x=data, m=model)


def load_test():
    np.random.seed(5)
    model = svm.svm_load_model("m2c/real.model")
    loader = DataLoader("data/csv/both")  # "csv/test"

    data = [{i + 1: line[i] for i in range(len(line))} for line in loader.have]
    tags = np.ones(len(data)).tolist()
    svm.svm_predict(y=tags, x=data, m=model)
    data = [{i + 1: line[i] for i in range(len(line))} for line in loader.test]
    tags = np.ones(len(data)).tolist()
    svm.svm_predict(y=tags, x=data, m=model)
    data = [{i + 1: line[i] for i in range(len(line))} for line in loader.none]
    tags = (np.ones(len(data)) * -1).tolist()
    svm.svm_predict(y=tags, x=data, m=model)


if __name__ == "__main__":
    load_test()