Add source files

src/elm/__init__.py

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+from .elm import ELMClassifier

src/elm/elm.py

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+import numpy as np
+from sklearn.base import BaseEstimator, TransformerMixin
+
+def softmax(x):
+    
+    max = np.max(x, axis=1, keepdims=True) # Returns max of each row and keeps same dims
+    e_x = np.exp(x - max) # Subtracts each row with its max value
+    sum = np.sum(e_x, axis=1, keepdims=True) # Returns sum of each row and keeps same dims
+    f_x = e_x / sum
+    
+    return f_x
+
+class ELMClassifier(BaseEstimator, TransformerMixin):
+
+    def __init__(self, L, random_state=None):
+        
+        self.L = L # number of hidden neurons
+        self.random_state = random_state # random state
+
+    def fit(self, X, y=None):
+
+        M = np.size(X, axis=0) # Number of examples
+        N = np.size(X, axis=1) # Number of features
+
+        np.random.seed(seed=self.random_state) # set random seed
+
+        self.w1 = np.random.uniform(low=-1, high=1, size=(self.L, N+1)) # Weights with bias
+
+        bias = np.ones(M).reshape(-1, 1) # Bias definition
+        Xa = np.concatenate((bias, X), axis=1) # Input with bias
+
+        S = Xa.dot(self.w1.T) # Weighted sum of hidden layer
+        H = np.tanh(S) # Activation function f(x) = tanh(x), dimension M X L
+
+        bias = np.ones(M).reshape(-1, 1) # Bias definition
+        Ha = np.concatenate((bias, H), axis=1) # Activation function with bias
+
+        # One-hot encoding
+        n_classes = len(np.unique(y))
+        y = np.eye(n_classes)[y]
+
+        self.w2 = (np.linalg.pinv(Ha).dot(y)).T # w2' = pinv(Ha)*D
+
+        return self
+
+    def predict(self, X):
+
+        M = np.size(X, axis=0) # Number of examples
+        N = np.size(X, axis=1) # Number of features
+
+        bias = np.ones(M).reshape(-1, 1) # Bias definition
+        Xa = np.concatenate((bias, X), axis=1) # Input with bias
+
+        S = Xa.dot(self.w1.T) # Weighted sum of hidden layer
+        H = np.tanh(S) # Activation function f(x) = tanh(x), dimension M X L
+
+        bias = np.ones(M).reshape(-1, 1) # Bias definition
+        Ha = np.concatenate((bias, H), axis=1) # Activation function with bias
+
+        y_pred = softmax(Ha.dot(self.w2.T)) # Predictions
+        
+        # Revert one-hot encoding
+        y_pred = np.argmax(y_pred, axis=1) # axis=1 means that we want to find the index of the maximum value in each row
+
+        return y_pred
+
+    def predict_proba(self, X):
+
+        M = np.size(X, axis=0) # Number of examples
+        N = np.size(X, axis=1) # Number of features
+
+        bias = np.ones(M).reshape(-1, 1) # Bias definition
+        Xa = np.concatenate((bias, X), axis=1) # Input with bias
+
+        S = Xa.dot(self.w1.T) # Weighted sum of hidden layer
+        H = np.tanh(S) # Activation function f(x) = tanh(x), dimension M X L
+
+        bias = np.ones(M).reshape(-1, 1) # Bias definition
+        Ha = np.concatenate((bias, H), axis=1) # Activation function with bias
+
+        y_pred = softmax(Ha.dot(self.w2.T)) # Predictions
+
+        return y_pred