def handwritingClassTest(train_X, train_y, test_X, test_y): clf = knn.KNNClassifier(k=3) len_test = len(test_y) error_count = 0.0 results = clf.classify(test_X, train_X, train_y) arr_res = np.array(results) - np.array(test_y) for elem in arr_res: error_count += np.abs(elem) error_rate = error_count / len_test print error_rate return error_rate
def __init__(self, norm_type="Normalization", iterations=5, base_classifier="SVM"): self.iterations = iterations self.norm_type = norm_type self.prediction = None self.probability = None self.classifier_set = None if base_classifier == "SVM": self.base_classifier = SVM.SVMClassifier() elif base_classifier == "KNN": self.base_classifier = KNN.KNNClassifier() elif base_classifier == "DecisionTree": self.base_classifier = DecisionTree.DecisionTreeClassifier() elif base_classifier == "Logistic": self.base_classifier = Logistic.LogisticRegressionClassifier() elif base_classifier == "Perceptron": self.base_classifier = Perceptron.PerceptronClassifier()
[float(param1), float(param2), float(param3), float(param4)]) xTest = np.vstack([xTest, newRow]) newTrue = np.array([classification]) trues = np.vstack([trues, newTrue]) count = count + 1 k = int(sys.argv[1]) timingFile = open(r"KNN/KNNTiming.txt", "a") # Call our python KNN implementation and time classification ourClass = KNN.KNNClassifier(xTrain, yTrain, k) ourStart = time.time() ourPred = ourClass.classify(xTest) ourEnd = time.time() ourTime = (ourEnd - ourStart) * 1000000 # Call the scikit KNN implementation and time classification scikitImp = neighbors.KNeighborsClassifier(k) sciStart = time.time() scikitImp.fit(xTrain, yTrain)