Python sklearn.svm.predict() Examples

The following are 6 code examples of sklearn.svm.predict(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.svm , or try the search function .
Example #1
Source File: audioTrainTest.py    From pyAudioAnalysis with Apache License 2.0 6 votes vote down vote up 
def regression_wrapper(model, model_type, test_sample):
    """
    This function is used as a wrapper to pattern classification.
    ARGUMENTS:
        - model:        regression model
        - model_type:        "svm" or "knn" (TODO)
        - test_sample:        a feature vector (np array)
    RETURNS:
        - R:            regression result (estimated value)

    EXAMPLE (for some audio signal stored in array x):
        TODO
    """
    if model_type == "svm" or model_type == "randomforest" or \
            model_type == "svm_rbf":
        return model.predict(test_sample.reshape(1,-1))[0]

    #    elif classifier_type == "knn":
    #    TODO
Example #2
Source File: svm-bagofWords.py    From TBBTCorpus with Apache License 2.0 5 votes vote down vote up 
def predict(self):
        vec = TfidfVectorizer(min_df=3,lowercase=True, sublinear_tf=True, use_idf=True,vocabulary=list(set(self.vocab)))
        train_vector = vec.fit_transform(self.train_data)
        print("Generating model")
        self.svm_classifier.fit(train_vector,self.train_labels)
        test_vector = vec.transform(self.test_data)
        print("Classifying Data")
        self.classification = self.svm_classifier.predict(test_vector)
Example #3
Source File: dataanalysis.py    From aurum-datadiscovery with MIT License 5 votes vote down vote up 
def compare_num_columns_dist_odsvm(svm, columnBdata):
    Xnumpy = np.asarray(columnBdata)
    X = Xnumpy.reshape(-1, 1)
    prediction_vector = svm.predict(X)
    return prediction_vector
Example #4
Source File: audioTrainTest.py    From pyAudioAnalysis with Apache License 2.0 5 votes vote down vote up 
def train_svm_regression(features, labels, c_param, kernel='linear'):
    svm = sklearn.svm.SVR(C=c_param, kernel=kernel)
    svm.fit(features, labels)
    train_err = np.mean(np.abs(svm.predict(features) - labels))
    return svm, train_err
Example #5
Source File: audioTrainTest.py    From pyAudioAnalysis with Apache License 2.0 5 votes vote down vote up 
def train_random_forest_regression(features, labels, n_estimators):
    rf = sklearn.ensemble.RandomForestRegressor(n_estimators=n_estimators)
    rf.fit(features, labels)
    train_err = np.mean(np.abs(rf.predict(features) - labels))
    return rf, train_err
Example #6
Source File: audioTrainTest.py    From pyAudioAnalysis with Apache License 2.0 4 votes vote down vote up 
def classifier_wrapper(classifier, classifier_type, test_sample):
    """
    This function is used as a wrapper to pattern classification.
    ARGUMENTS:
        - classifier:        a classifier object of type sklearn.svm.SVC or 
                             kNN (defined in this library) or sklearn.ensemble.
                             RandomForestClassifier or sklearn.ensemble.
                             GradientBoostingClassifier  or 
                             sklearn.ensemble.ExtraTreesClassifier
        - classifier_type:   "svm" or "knn" or "randomforests" or 
                             "gradientboosting" or "extratrees"
        - test_sample:        a feature vector (np array)
    RETURNS:
        - R:            class ID
        - P:            probability estimate

    EXAMPLE (for some audio signal stored in array x):
        import audioFeatureExtraction as aF
        import audioTrainTest as aT
        # load the classifier (here SVM, for kNN use load_model_knn instead):
        [classifier, MEAN, STD, classNames, mt_win, mt_step, st_win, st_step] =
        aT.load_model(model_name)
        # mid-term feature extraction:
        [mt_features, _, _] = aF.mtFeatureExtraction(x, Fs, mt_win * Fs,
        mt_step * Fs, round(Fs*st_win), round(Fs*st_step));
        # feature normalization:
        curFV = (mt_features[:, i] - MEAN) / STD;
        # classification
        [Result, P] = classifierWrapper(classifier, model_type, curFV)
    """
    class_id = -1
    probability = -1
    if classifier_type == "knn":
        class_id, probability = classifier.classify(test_sample)
    elif classifier_type == "svm" or \
            classifier_type == "randomforest" or \
            classifier_type == "gradientboosting" or \
            classifier_type == "extratrees" or \
            classifier_type == "svm_rbf":
        class_id = classifier.predict(test_sample.reshape(1, -1))[0]
        probability = classifier.predict_proba(test_sample.reshape(1, -1))[0]
    return class_id, probability