"""Support vector machine python code
"""

import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np

style.use('ggplot')


class SupportVectorMachine:
    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)

    def fit(self, data):
        self.data = data
        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)

        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
            while not optimized:
                for b in np.arange(-1*(self.max_feature_value*b_range_multiple),
                                   self.max_feature_value*b_range_multiple,
                                   step*b_multiple):
                    for transformation in transforms:
                        w_t = w * transformation
                        found_option = True
                        for i in self.data:
                            for xi in self.data[i]:
                                yi = i
                                if not yi*(np.dot(w_t, xi)+b) >= 1:
                                    found_option = False
                        if found_option:
                            opt_dict[np.linalg.norm(w_t)] = [w_t, b]
                if w[0] < 0:
                    optimized = True
                    print('Optimized a step')
                else:
                    w = w - step
            norms = sorted([n for n in opt_dict])
            opt_choice = opt_dict[norms[0]]
            self.w = opt_choice[0]
            self.b = opt_choice[1]
            latest_optimum = opt_choice[0][0]+step*2
            
    def predict(self, features):
        classification = np.sign(np.dot(np.array(features), self.w)+self.b)
        if classification != 0 and self.visualization:
            self.ax.scatter(features[0], features[1], s=200, marker='*', c=self.colors[classification])
        return classification

    def visualize(self):
        [[self.ax.scatter(x[0], x[1], s=100, color=self.colors[i]) for x in data_dict[i]] for i in data_dict]

        def hyperplane(x, w, b, v):
            return (-w[0] * x - b + v) / w[1]

        data_range = (self.min_feature_value*0.9, self.max_feature_value*1.1)
        hyp_x_min = data_range[0]
        hyp_x_max = data_range[1]

        psv1 = hyperplane(hyp_x_min, self.w, self.b, 1)
        psv2 = hyperplane(hyp_x_max, self.w, self.b, 1)
        self.ax.plot([hyp_x_min, hyp_x_max], [psv1, psv2], 'k')

        nsv1 = hyperplane(hyp_x_min, self.w, self.b, -1)
        nsv2 = hyperplane(hyp_x_max, self.w, self.b, -1)
        self.ax.plot([hyp_x_min, hyp_x_max], [nsv1, nsv2], 'k')

        dv1 = hyperplane(hyp_x_min, self.w, self.b, 0)
        dv2 = hyperplane(hyp_x_max, self.w, self.b, 0)
        self.ax.plot([hyp_x_min, hyp_x_max], [dv1, dv2], 'w--')

        plt.show()

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

svm = SupportVectorMachine()
svm.fit(data=data_dict)

predict_us = [[0, 10],
              [1, 3],
              [3, 4],
              [3, 5],
              [5, 5],
              [5, 6],
              [6, -5],
              [5, 8]]

for p in predict_us:
    svm.predict(p)

svm.visualize()
