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
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)
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)
###########################################
## 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]