コード例 #1
0
    # CRF model
    model_crf = sklearn_crfsuite.CRF(algorithm='lbfgs',
                                     c1=0.25,
                                     c2=0.01,
                                     max_iterations=100,
                                     all_possible_transitions=True)

    # train the model
    model_crf.fit(X_train, y_train)
    labels = list(model_crf.classes_)

    # preditcion of the labels of the test sequence
    y_pred = model_crf.predict(X_test)

    # print output time remarks
    outputSteps(y_pred[0])

    # calculate probability
    def get_prob(y_prob):
        prob_actions = []
        for i in y_prob:
            for x in i:
                prob_actions.append(x[max(x, key=x.get)])
            prob_seq = prod(prob_actions)
        return prob_seq

    # Probability to model sequence
    y_prob = model_crf.predict_marginals(X_test)
    prob_results.append(get_prob(y_prob))

    # Probability to model random sequence
コード例 #2
0
    # flatten ground_truth of trainingsdata
    y_train_ground_truth_flatten = [
        item for sublist in y_train_ground_truth for item in sublist
    ]

    # Gaussian Naive Bayes (GaussianNB)
    model_bayes = GaussianNB()

    # training
    model_bayes.fit(X_train_flatten, y_train_ground_truth_flatten)

    # state prediction
    y_pred_bayes = model_bayes.predict(X_test_flatten)

    # print output time remarks
    outputSteps(y_pred_bayes)

    # probability calculation
    def get_prob(y_prob):
        prob_actions = []
        for i in y_prob:
            prob_actions.append(max(i))
        prob_seq = sum(prob_actions)
        return prob_seq

    # Probability to model sequence
    y_prob = model_bayes.predict_log_proba(X_test_flatten)
    prob_results.append(math.exp(get_prob(y_prob)))

    # Probability to model random sequence
    y_prob_random_seq = model_bayes.predict_log_proba(X_random)
コード例 #3
0
    # change type to array
    seqlearn_X_train = np.array(X_train_flatten)
    seqlearn_y_ground_truth = np.array(y_ground_truth_flatten)

    # HMM seqlearn MultimodalHMM
    model_seqlearn = MultinomialHMM()

    # training
    model_seqlearn.fit(seqlearn_X_train, seqlearn_y_ground_truth, len_train)

    # state prediction
    y_pred_seqlearn = model_seqlearn.predict(X_test_flatten)

    # print output time remarks
    outputSteps(y_pred_seqlearn)

    #state prediction for random sequence
    y_pred_seqlearn_random = model_seqlearn.predict(X_random)

    # state prediction for heuristic sequence
    y_pred_seqlearn_random = model_seqlearn.predict(X_heuristic)

    # target names for evaluation
    target_names = list(step_set)
    target_names = sorted(target_names)

    # confusion matrix
    cnf_matrix = confusion_matrix(y_ground_truth, y_pred_seqlearn)
    normalize = cnf_matrix.astype('float') / cnf_matrix.sum(axis=1)[:,
                                                                    np.newaxis]