NapkinMLNumPy 机器学习模型的袖珍实现
NapkinML 是 NumPy 中机器学习模型的袖珍实现。
安装
$ git clone https://github.com/eriklindernoren/NapkinML
$ cd NapkinML
$ sudo python setup.py install
实现
K-Nearest 最近邻
class KNN():
def predict(self, k, Xt, X, y):
y_pred = np.empty(len(Xt))
for i, xt in enumerate(Xt):
idx = np.argsort([np.linalg.norm(x-xt) for x in X])[:k]
y_pred[i] = np.bincount([y[i] for i in idx]).argmax()
return y_pred
$ python napkin_ml/examples/knn.py
图：使用K-Nearest最近邻的Iris数据集的分类。
线性回归
class LinearRegression():
def fit(self, X, y):
self.w = np.linalg.lstsq(X, y, rcond=None)[0]
def predict(self, X):
return X.dot(self.w)
$ python napkin_ml/examples/linear_regression.py
图：线性回归
线性判别分析
class LDA():
def fit(self, X, y):
cov_sum = sum([np.cov(X[y == val], rowvar=False) for val in [0, 1]])
mean_diff = X[y == 0].mean(0) - X[y == 1].mean(0)
self.w = np.linalg.inv(cov_sum).dot(mean_diff)
def predict(self, X):
return 1 * (X.dot(self.w) < 0)
$ python napkin_ml/examples/lda.py
Logistic 回归
class LogisticRegression():
def fit(self, X, y, n_iter=4000, lr=0.01):
self.w = np.random.rand(X.shape[1])
for _ in range(n_iter):
self.w -= lr * (self.predict(X) - y).dot(X)
def predict(self, X):
return sigmoid(X.dot(self.w))
$ python napkin_ml/examples/logistic_regression.py
图：Logistic回归分类
多层感知器
class MLP():
def fit(self, X, y, n_epochs=4000, lr=0.01, n_units=10):
self.w = np.random.rand(X.shape[1], n_units)
self.v = np.random.rand(n_units, y.shape[1])
for _ in range(n_epochs):
h_out = sigmoid(X.dot(self.w))
out = softmax(h_out.dot(self.v))
self.v -= lr * h_out.T.dot(out - y)
self.w -= lr * X.T.dot((out - y).dot(self.v.T) * (h_out * (1 - h_out)))
def predict(self, X):
return softmax(sigmoid(X.dot(self.w)).dot(self.v))
$ python napkin_ml/examples/mlp.py
图：带有一个隐藏层的多层感知器对虹膜数据集的分类。
主成分分析
class PCA():
def transform(self, X, dim):
_, S, V = np.linalg.svd(X - X.mean(0), full_matrices=True)
idx = S.argsort()[::-1]
V = V[idx][:dim]
return X.dot(V.T)
$ python napkin_ml/examples/pca.py
图：主成分分析降维。
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