A tiny lib with pocket-sized implementations of machine learning models in NumPy.
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napkin_ml K-Means line reduction Sep 8, 2018
.gitignore Initial commit Jan 25, 2018
LICENSE MIT Licence Jan 26, 2018
README.md Update README.md Sep 8, 2018
requirements.txt Reduced K-Means Sep 8, 2018
setup.py Initial commit Jan 25, 2018

README.md

NapkinML

About

Pocket-sized implementations of machine learning models.

Table of Contents

Installation

$ git clone https://github.com/eriklindernoren/NapkinML
$ cd NapkinML
$ sudo python setup.py install

Implementations

K-Means

class KMeans:
    def fit(self, X, k, n_iter=200):
        centers = random.sample(list(X), k)
        for i in range(n_iter):
            clusters = np.argmin(cdist(X, centers), axis=1)
            centers = np.array([X[clusters == c].mean(0) for c in clusters])
        return clusters
$ python napkin_ml/examples/kmeans.py

Figure: K-Means clustering of the Iris dataset.

K-Nearest Neighbors

class KNN:
    def predict(self, k, Xt, X, y):
        idx = np.argsort(cdist(Xt, X))[:, :k]
        y_pred = [np.bincount(y[i]).argmax() for i in idx]
        return y_pred
$ python napkin_ml/examples/knn.py

Figure: Classification of the Iris dataset with K-Nearest Neighbors.

Linear Regression

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

Figure: Linear Regression.

Linear Discriminant Analysis

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)

Logistic Regression

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

Figure: Classification with Logistic Regression.

Multilayer Perceptron

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

Figure: Classification of the Iris dataset with a Multilayer Perceptron
with one hidden layer.

Principal Component Analysis

class PCA:
    def transform(self, X, dim):
        _, S, V = np.linalg.svd(X - X.mean(0), full_matrices=True)
        idx = S.argsort()[::-1][:dim]
        return X.dot(V[idx].T)
$ python napkin_ml/examples/pca.py

Figure: Dimensionality reduction with Principal Component Analysis.