手写bp神经网络

from scipy.io import arff
import numpy as np
import pandas as pd

radius = 4  # search radius


def distance(point1, point2):
    return np.sqrt(np.sum([(point1[i] - point2[i]) ** 2 for i in range(4)]))


iris = arff.loadarff(\'../dataset/iris.arff\')
df = pd.DataFrame(iris[0])
length = df.shape[0]
classes = list(set(df[\'class\']))
classes_length = len(classes)
classes_dict = dict()
for i in range(classes_length):
    classes_dict[classes[i]] = i
for i in range(length):
    df.iloc[i, 4] = classes_dict[df.iloc[i, 4]]
    if df.iloc[i, 4] != 0:  # convert to an binary classification
        df.iloc[i, 4] = 1
# df = df.sample(frac=1)  # shuffle data randomly
train_data = df.iloc[0:100]
test_data = df.iloc[100:]
train_length = train_data.shape[0]
test_length = test_data.shape[0]
accuracy = 0


def my_iter(data, batch):
    X = []
    y = []
    length = data.shape[0]
    start = 0
    while length - batch > 0:
        X = data.iloc[start:start + batch, 0:4]
        y = data.iloc[start:start + batch, 4]
        yield X, y
        start += batch
        length -= batch
    X = data.iloc[start:, 0:4]
    y = data.iloc[start:, 4]
    yield X, y


def sigmoid(z):
    return 1 / (1 + np.exp(-z))


def initialize_with_zeros(dim):
    w = np.zeros([dim, 1])  # input=4, neural=1
    b = 0
    return w, b


# w 4,1
# X batch 4
def forward(w, b, X, Y):
    batch = X.shape[0]
    Z = np.dot(X, w) + b
    Z.resize([batch])
    A = sigmoid(Z)
    cost = -1 / batch * np.sum(Y * np.log(A) + (1 - Y) * np.log(1 - A))
    return cost, A


# w 4,1
# X batch 4
# A batch 1
def backward(w, b, X, Y, A, cost):
    batch = X.shape[0]
    Y = np.array(Y)
    Y.resize(batch, 1)
    A.resize(batch, 1)
    dw = np.array(1 / batch * np.sum(np.multiply(X, (A - Y)), axis=0))
    db = np.array(1 / batch * np.sum(A - Y))
    dw.resize([4, 1])
    cost = np.squeeze(cost)
    grads = {\'dw\': dw, \'db\': db}
    return grads, cost


def optimize(w, b, train_data, learning_rate, batch=20):
    cost = []
    for i, (X, Y) in enumerate(my_iter(train_data, batch)):
        cost, A = forward(w, b, X, Y)
        grads, cost = backward(w, b, X, Y, A, cost)
        dw = grads[\'dw\']
        db = grads[\'db\']
        # renew the parameters
        # w 4,1
        # dw 4,1
        w = w - learning_rate * dw
        b = b - learning_rate * db
        print(\'iter %d loss %f\' % (i, cost))
        params = {\'w\': w, \'b\': b}
    return params


def predict(w, b, X):
    batch = X.shape[0]
    y_predict = np.zeros((1, batch))
    y_predict = sigmoid(np.dot(X, w) + b)
    for i in range(batch):
        if y_predict[i, 0] > 0.5:
            y_predict[i, 0] = 1
        else:
            y_predict[i, 0] = 0
    return y_predict


w, b = initialize_with_zeros(4)
params = optimize(w, b, train_data, learning_rate=0.1, batch=20)

y_predict_train = predict(w, b, train_data.iloc[:, 0:4])
y_predict_test = predict(w, b, test_data.iloc[:, 0:4])
y_predict_train.resize([train_length])
y_predict_test.resize([test_length])
print(\'acc train %.2f\' % (np.mean(y_predict_train == train_data.iloc[:, 4])))
print(\'acc test %.2f\' % (np.mean(y_predict_test == test_data.iloc[:, 4])))