BasicSVM.py

# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""

import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np 
style.use('ggplot')

class Support_Vector_Machine:
    def __init__(self, visualization=True):
        self.visualization = visualization
        self.colors = (1:'r',-1:'b')
        if self.visualization:
            self.fig = plt.figure()
            self.ax = self.fig.add_subplot(1,1,1)
    
    #train
    def fit(self, data):
        self.data = data
        # { ||w||: [w,b] }
        opt_dict = {}
        
        transforms = [[1,1],[-1,1],[-1,-1],[1,-1]]
        
        all_data = []
        for yi in self.data:
            for featureset in self.data[yi]:
                for feature in featureset:
                    all_data.append(feature)
                    
        self.max_feature_value = max(all_data)
        self.min_feature_value = min(all_data)
        all_data = None
        
        step_sizes = [self.max_feature_value * 0.1,
                      self.max_feature_value * 0.01,
                      #point of expense.
                      self.max_feature_value * 0.001]
    
        #extremely expensive
        b_range_multiple = 5
        
        b_multiple = 5
        
        latest_optimum = self.max_feature_value*10
        
        for step in step_sizes:
            w= np.array([latest_optimum, latest_optimum])
            #we can do this because convex
            optimized = False
            while not optimized :
                pass
    
    def predict(self, features):
        # sign(x_i.w+b)
        classification = np.sign(np.dot(np.array(features),self.w)+self.b)
        
        return classification



data_dict = (-1:np.array([[1,7],[2,8],[3,8]]),1:np.array([[5,1],[6,-1],[7,3]]))