コード例 #1
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    def __init__(self, dimensions=0, k_neighbors=5, use_kernel=False):
        '''
        Constructor for FaceRecognizer.

        Args (optional):
            k_rank (int): How many principal components to keep.
            k_neighbors (int): How many neighbors to compare against in the
                kNN classifier.
        '''

        self.pca_model = PCAModel(dimensions=dimensions, use_kernel=use_kernel)
        self.knn_classifier = KNNClassifier(neighbors=k_neighbors)
        self.instances = None
コード例 #2
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def main():
    df_beijing = pd.read_csv(BEIJING_PATH)
    df_shenyang = pd.read_csv(SHENYANG_PATH)
    X, Y = prepare_data_and_labels(df_beijing, df_shenyang)

    clf = KNNClassifier(K=5)
    validate(clf, X, Y)

    print("GUANGZHOU")
    df_guangzhou = pd.read_csv(GUANGZHOU_PATH)
    X_test, Y_test = prepare_data_and_labels(df_guangzhou)
    test(clf, X_test, Y_test)

    print("SHANGHAI")
    df_shanghai = pd.read_csv(SHANGHAI_PATH)
    X_test, Y_test = prepare_data_and_labels(df_shanghai)
    test(clf, X_test, Y_test)
コード例 #3
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import numpy as np
import KNNClassifier

import matplotlib.pyplot as plt
if __name__ == '__main__':
    image_size = 28  # width and length
    no_of_different_labels = 10  #  i.e. 0, 1, 2, 3, ..., 9
    image_pixels = image_size * image_size
    data_path = "/mnist/"
    train_data = np.loadtxt(data_path + "mnist_train.csv", delimiter=",")
    test_data = np.loadtxt(data_path + "mnist_test.csv", delimiter=",")
    fac = 0.99 / 255
    train_imgs = np.asfarray(train_data[:, 1:]) * fac + 0.01
    test_imgs = np.asfarray(test_data[:, 1:]) * fac + 0.01

    train_labels = np.asfarray(train_data[:, :1])
    test_labels = np.asfarray(test_data[:, :1])

    knn = KNNClassifier(distance='Euclidean', K=5)
    knn.fit(train_imgs, train_labels)
    results = knn.predict(test_imgs)

    #
    # for i in range(10):
    #     img = train_imgs[i].reshape((28,28))
    #     plt.imshow(img, cmap="Greys")
    #     plt.show()

    from sklearn.metrics import confusion_matrix
    confusion_matrix(test_labels, results)
コード例 #4
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###########################################
## KNN test kodları

from sklearn.datasets import load_iris
from sklearn.utils import shuffle

iris_X, iris_y = load_iris(return_X_y=True)
iris_X, iris_y = shuffle(iris_X, iris_y)
X_train = iris_X[:-30]
X_test = iris_X[-30:]
y_train = iris_y[:-30]
y_test = iris_y[-30:]

from KNNClassifier import *

knn = KNNClassifier("eucledean", 10)
knn.buildModel(X_train, y_train)
knn.evaluateModel(X_test, y_test)
knn.showLabel(X_test[5], load_iris())

#########################################
## Naive bayes test kodları

from sklearn.datasets import load_iris
from sklearn.utils import shuffle

iris_X, iris_y = load_iris(return_X_y=True)
iris_X, iris_y = shuffle(iris_X, iris_y)
X_train = iris_X[:-30]
X_test = iris_X[-30:]
y_train = iris_y[:-30]