Initial commit

ML/algorithms/naivebayes/example_data/data.txt

@@ -0,0 +1,90 @@
+1.1107,   -2.1079
+-0.5498,    0.0943
+-0.0382,    1.8829
+0.0555,   -0.6139
+0.5870,   -1.2067
+0.5453,    0.2509
+-0.3927,   -0.6220
+-1.1905,   -1.8785
+-0.4240,    0.7772
+-0.7139,    1.5846
+-0.8883,    2.1408
+-0.6922,    0.0993
+1.4350,    1.2334
+-0.7576,    0.7386
+-1.1144,   -1.7059
+0.6612,   -1.7296
+-2.1381,   -0.0600
+1.3857,    1.2178
+-1.4951,    0.0373
+0.8029,    0.9739
+1.5607,    1.5862
+0.8563,   -1.4245
+0.0397,   -1.3799
+1.2331,    1.7421
+-2.0015,    0.8355
+-0.3428,   -0.4780
+-0.8891,    1.2634
+0.3832,   -0.1189
+0.4172,    1.0132
+-0.8695,   -0.7947
+2.9737,    3.6438
+3.7680,    1.8649
+0.1166,    0.9435
+0.6896,    3.9160
+1.2234,    2.9899
+2.3009,    0.4150
+3.7693,    3.8027
+1.9450,    3.4208
+0.9290,    3.3611
+5.0027,    2.7870
+1.0101,    1.8737
+2.0751,    2.2628
+1.9113,    3.6777
+2.3127,    3.9130
+1.9392,    2.3976
+3.1218,    2.5495
+1.7032,    1.1509
+0.4212,    3.5322
+2.7686,    0.9402
+2.1696,    2.9285
+0.3380,    2.0947
+3.6886,    0.4054
+2.6315,    3.1962
+-0.5332,    3.1421
+0.3380,    3.0801
+1.4030,    1.1841
+2.8739,    2.7777
+1.1254,    3.2404
+0.0988,    1.9522
+0.3688,    2.8904
+1.4758,   -1.6387
+1.9289,   -1.8191
+2.5741,   -1.3213
+2.1917,   -1.2852
+0.8358,   -2.3349
+2.6863,   -1.8834
+3.1102,   -0.4854
+3.7073,   -0.6466
+3.6394,   -0.4097
+0.5365,   -3.6555
+2.9295,   -0.3819
+0.8168,   -3.1133
+1.3432,   -1.7717
+1.1039,   -2.2261
+1.3754,   -2.2236
+0.6757,   -2.5379
+-0.2029,   -3.8420
+2.4210,   -1.9788
+1.0335,   -2.6042
+0.9638,   -2.9449
+-0.8198,   -5.4449
+1.9552,   -1.5530
+0.3505,   -3.1887
+2.4943,   -1.8116
+1.9761,   -1.0664
+0.5994,   -3.0513
+2.2076,   -1.6728
+1.9941,   -1.8826
+1.7487,   -2.9644
+1.4160,   -2.4234

ML/algorithms/naivebayes/example_data/targets.txt

@@ -0,0 +1,90 @@
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3
+3

ML/algorithms/naivebayes/naivebayes.py

@@ -0,0 +1,67 @@
+"""
+Naive Bayes Classifier Implementation from scratch
+
+To run the code structure the code in the following way:
+    X be size: (num_training_examples, num_features)
+    y be size: (num_classes, )
+
+Where the classes are 0, 1, 2, etc. Then an example run looks like:
+    NB = NaiveBayes(X, y)
+    NB.fit(X)
+    predictions = NB.predict(X)
+
+Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
+*    2020-04-21 Initial coding
+
+"""
+import numpy as np
+
+
+class NaiveBayes:
+    def __init__(self, X, y):
+        self.num_examples, self.num_features = X.shape
+        self.num_classes = len(np.unique(y))
+        self.eps = 1e-6
+
+    def fit(self, X):
+        self.classes_mean = {}
+        self.classes_variance = {}
+        self.classes_prior = {}
+
+        for c in range(self.num_classes):
+            X_c = X[y == c]
+
+            self.classes_mean[str(c)] = np.mean(X_c, axis=0)
+            self.classes_variance[str(c)] = np.var(X_c, axis=0)
+            self.classes_prior[str(c)] = X_c.shape[0] / X.shape[0]
+
+    def predict(self, X):
+        probs = np.zeros((self.num_examples, self.num_classes))
+
+        for c in range(self.num_classes):
+            prior = self.classes_prior[str(c)]
+            probs_c = self.density_function(
+                X, self.classes_mean[str(c)], self.classes_variance[str(c)]
+            )
+            probs[:, c] = probs_c + np.log(prior)
+
+        return np.argmax(probs, 1)
+
+    def density_function(self, x, mean, sigma):
+        # Calculate probability from Gaussian density function
+        const = -self.num_features / 2 * np.log(2 * np.pi) - 0.5 * np.sum(
+            np.log(sigma + self.eps)
+        )
+        probs = 0.5 * np.sum(np.power(x - mean, 2) / (sigma + self.eps), 1)
+        return const - probs
+
+
+if __name__ == "__main__":
+    X = np.loadtxt("example_data/data.txt", delimiter=",")
+    y = np.loadtxt("example_data/targets.txt") - 1
+
+    NB = NaiveBayes(X, y)
+    NB.fit(X)
+    y_pred = NB.predict(X)
+
+    print(f"Accuracy: {sum(y_pred==y)/X.shape[0]}")