jad2192

import numpy as np

class N_Tuple_Classifier_fast(object):
    
    def __init__(self, pixel_percentage=0.1, num_tuples=100, pixel_tolerance=0.3, warm_start=None):
        ''' pixel_percentage: The percentage of pixels on to which we'll 
                              For example the default 0.1 randomly choose 78 pixels.
                       
            num_tuples: Number of unique tuples on to which project.
            

jad2192

import numpy as np
from numpy import random as rnd
import cv2

def gen_data(N=100, L=8, NData=160, NRows=1, Label_Hsize=50, Label_sigma=7, 
             Noise_sigma=4):
    
    Label_Hsize += (1 - (Label_Hsize % 2 ))   # cv2 Gaussian filter requires odd dimensions
    Labels = np.zeros((NData, N))
    Features = np.zeros((NData, NRows, N, L))

jad2192

jad2192

# Most of these algorithms are derived from psuedocode in Cormen et. al. 

inf = float('inf')

class Vertex(object):
	""" Adjacent list implementation of a graph. Create a vertex (v) and then its adjacent edges are stored
	in the list v.edges. A graph will then just be a set of vertex objects. Can use this to implement
	directed/undirected and weighted/unweighted graphs easily."""

	def __init__(self, adj=[]):