Python sklearn.svm.predict() Examples

The following are 6 code examples for showing how to use sklearn.svm.predict(). These examples are extracted from open source projects. 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.

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Example 1
Project: pyAudioAnalysis   Author: tyiannak   File: audioTrainTest.py    License: 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
Project: TBBTCorpus   Author: skashyap7   File: svm-bagofWords.py    License: 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
Project: aurum-datadiscovery   Author: mitdbg   File: dataanalysis.py    License: 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
Project: pyAudioAnalysis   Author: tyiannak   File: audioTrainTest.py    License: 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
Project: pyAudioAnalysis   Author: tyiannak   File: audioTrainTest.py    License: 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
Project: pyAudioAnalysis   Author: tyiannak   File: audioTrainTest.py    License: 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