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ex2.py

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+import numpy as np
+import sys
+from random import randint
+from numpy import linalg as LA
+import random
+
+
+class Perceptron():
+    def __init__(self, x_train, y_train):
+        self.x_train = x_train
+        self.y_train = y_train
+        self._eta = 0.01
+        self._epochs = 20
+
+    def train(self):
+        w = np.zeros([3, 8])
+        for e in range(self._epochs):
+            mistake = 0
+            for i in range(len(self.x_train)):
+                x, y = shuffle(self.x_train, self.y_train)
+                y_hat = int(np.argmax(np.dot(w, x)))
+                if y != y_hat:
+                    mistake += 1
+                    w[y, :] = w[y, :] + self._eta * x
+                    w[y_hat, :] = w[y_hat, :] - self._eta * x
+        return w, mistake
+
+
+class SVM():
+    def __init__(self, x_train, y_train):
+        self.x_train = x_train
+        self.y_train = y_train
+        self._epochs = 20
+        self._lambda = 0.001
+        self._eta = 0.01
+
+    def train(self):
+        w = np.zeros([3, 8])
+        for e in range(self._epochs):
+            mistake = 0
+            for i in range(len(self.x_train)):
+                #shuffle
+                x, y = shuffle(self.x_train, self.y_train)
+                #predict
+                y_hat = int(np.argmax(np.dot(w, x)))
+                #update
+                if y != y_hat:
+                    mistake += 1
+                    w[y, :] = w[y, :] * (1 - self._eta * self._lambda) + self._eta * x
+                    w[y_hat, :] = w[y_hat, :] * (1 - self._eta * self._lambda) - self._eta * x
+        return w, mistake
+
+class PassiveAggressive():
+    def __init__(self, x_train, y_train):
+        self.x_train = x_train
+        self.y_train = y_train
+        self._epochs = 25
+
+    def train(self):
+        w = np.zeros([3, 8])
+        for e in range(self._epochs):
+            mistake = 0
+            for i in range(len(self.x_train)):
+                x, y = shuffle(self.x_train, self.y_train)
+                # predict
+                y_hat = int(np.argmax(np.dot(w, x)))
+                # update
+                if y != y_hat:
+                    mistake += 1
+                    loss = max(0, 1 - np.dot(w[y, :], x) + np.dot(w[y_hat, :], x))
+                    tau = loss / (2 * np.power(LA.norm(x), 2))
+                    w[y, :] = w[y, :] + tau * x
+                    w[y_hat, :] = w[y_hat, :] - tau * x
+        return w, mistake
+
+def shuffle(x,y):
+    p = randint(0,len(x)-1)
+    return x[p],int(y[p])
+
+
+def load_data():
+    train_x = open(sys.argv[1], 'r')
+    train_x = np.loadtxt(train_x.name, dtype=str, delimiter=",")
+    replaceCharsForInts(train_x)
+    train_x = train_x.astype(np.float)
+
+    train_y = open(sys.argv[2], 'r')
+    train_y = np.loadtxt(train_y.name, delimiter=",")
+
+    test_x = open(sys.argv[3], 'r')
+    test_x = np.loadtxt(test_x.name, dtype=str, delimiter=',')
+    replaceCharsForInts(test_x)
+    test_x = test_x.astype(np.float)
+
+    test_y = open(sys.argv[4], 'r')
+    test_y = np.loadtxt(test_y.name, dtype=int, delimiter=',')
+    return train_x, train_y, test_x,test_y
+
+def convertCharToNum(c):
+    if c =='M':
+        return 1.0
+    elif c == 'F':
+        return 2.0
+    else:#'I'
+        return 3.0
+
+def replaceCharsForInts(l):
+    for col in l:
+        res = convertCharToNum(col[0])
+        col[0] = np.float(res)
+
+def test(test_x, test_y):
+    miss = 0
+    for i in range(len(test_x)):
+        res = int(np.argmax(np.dot(w, test_x[i])))
+        if res != test_y[i]:
+            miss += 1
+    return miss
+
+train_x, train_y, test_x,test_y = load_data()
+percep = Perceptron(train_x, train_y)
+svm = SVM(train_x, train_y)
+pa = PassiveAggressive(train_x, train_y)
+
+algos = [percep, svm, pa]
+for algo in algos:
+    w, mistakes = algo.train()
+    miss = test(test_x, test_y)
+    print(algo.__class__.__name__, "accuracy:", "{0:.2f}%".format((1 - miss / len(test_x)) * 100))