refactor

vanilla_logistic_regression.py

@@ -1,46 +0,0 @@
-import numpy as np
-
-"""
-# Logistic Regression
-
-w^T · x = w0 + w1x1 + w2x2 + w3x3
-
-P(A|x) = σ(w^Tx)
-
-# Given data:
-
-D = ((x1, y1), ..., (xn, yn))
-x ∈ R
-y ∈ {0, 1}
-
-
-# Pro's:
-
-- interpretable
-- small # params (d + 1)
-- computationally efficient
-- extensible to multi-class
-
-# Cons
-
-- performance as good as other models
-
-# Maximum Likelihood Estimation MLE
-
-labels y
-input  x
-
-likelihood lw = P(y[i]| x[i], w) * P(y[j] | x[j], w) ... P(y[n] | x[n],w)
-
-in product notation
-
-lw = 
-"""
-
-
-def sigmoid(z):
-    """
-    :param z: w^Tx (scalar)
-    :return: σ(w^Tx) (scalar)
-    """
-    return 1 / (1 + np.exp(-z))