writing_svm.py

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)

    #training
    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 features in featureset:
                    all_data.append(features)

        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,
                      self.max_feature_value * 0.001,]

        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])
            optimized = False
            



    def predict(self,data):
        # sign(x.w+b)
        classification = np.sign(np.dot(np.array(features),self.w)+self.b)
        
        return classification





data_dict = {-1:np.array([[1,7],
                          [2,0]
                          [3,0],]),
                          
            1:np.array([[5,1],
                        [6,-1],
                        [7,3],])}