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Commit 7e06a89e authored by s183919's avatar s183919
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Merge branch 'master' of https://lab.compute.dtu.dk/s183927/color-classification

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main (logreg - prediction only).py 0 → 100644
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# -*- coding: utf-8 -*-
# Text Color Chooser (Logistic Regression) - Prediction Only: Main
import pygame
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import pyplot
from mpl_toolkits.mplot3d import Axes3D
from random import randint
from strToFile import stringToFile
from random import seed
from sklearn.linear_model import LogisticRegression
# init
pygame.init()
# resolution
width = 800
height = 600
# make the pygame window
display = pygame.display.set_mode((width, height))
# set framerate
clock = pygame.time.Clock()
clock.tick(60)
# keyboard pictures and predictor
leftPic = pygame.image.load('left.png')
rightPic = pygame.image.load('right.png')
upPic = pygame.image.load('up.png')
predPic = pygame.image.load('predictor.png')
iconPic = pygame.image.load('icon.png')
downPic = pygame.image.load('down.png')
# set window icon
pygame.display.set_icon(iconPic)
# title of app
pygame.display.set_caption('Text Color Chooser (Logistic Regression) v. 1.0 - Prediction - Training Data Sample Size: 384')
# set fonts
smallfont = pygame.font.SysFont('comicsansms', 25)
mediumfont = pygame.font.SysFont('comicsansms', 50)
largefont = pygame.font.SysFont('comicsansms', 80)
# matrixes of color, index 0 should be deleted
points = np.reshape(np.array([[0, 0, 0],[255,255,255]]), (-1, 3))
white = np.reshape(np.array([0, 0, 0]), (-1, 3))
black = np.reshape(np.array([255, 255, 255]), (-1, 3))
# initial arrays for correct and total predictions for vstack
corcnt = np.array([1])
incnt = np.array([1])
# ////////////////////////////////////////////////////////////
# write on screen
# ////////////////////////////////////////////////////////////
def text(msg,color,font,x,y):
screen_text = font.render(msg, True, color)
display.blit(screen_text, [x, y])
# program loop
number = 0
while True:
# random color
color = np.array([randint(0,255), randint(0,255), randint(0,255)])
number += 1
# color background
display.fill((123, 123, 123))
# display information
text('Total Predictions: '+str(number-2), (255,255,255), smallfont, width / 1.5 - -135 / 2, 210)
# draw color boxes
pygame.draw.rect(display, color, [width / 3 - 200 / 2, 300, 200, 200])
pygame.draw.rect(display, color, [width / 1.5 - 200 / 2, 300, 200, 200])
pygame.draw.rect(display, (37,37,37), [width / 3 - 220 / 2 - 4, 75, 220, 40])
# blit pics of arrows
display.blit(leftPic, (width / 3 - 63 / 2, 512))
display.blit(rightPic, (width / 1.5 - 63 / 2, 512))
display.blit(downPic, ((width / 3 - 63 / 2), 125))
# text
text('Text', (255,255,255), largefont, width / 3 - 200 / 2 + 20, 330)
text('Text', (0,0,0), largefont, width / 1.5 - 200 / 2 + 20, 330)
text('Save Prediction Data', (255,255,255), smallfont, width / 3 - 216 / 2, 75)
text('RGB Color Value: ',(255,255,255), smallfont, 5, 200)
text(str(color), (255,255,255), smallfont, 5, 230)
text('press tab to show data', (255,255,255), smallfont, width - 275, 10)
# convert RGB values to between 0 and 1
z = color/255
# training data (sample size: 384)
X = [[0.729411764705882, 0.647058823529412, 0.945098039215686], [0.482352941176471, 0.443137254901961, 0.0862745098039216], [0.643137254901961, 0.525490196078431, 0.313725490196078], [0.462745098039216, 0.933333333333333, 0.501960784313725], [0.588235294117647, 0.168627450980392, 0.768627450980392], [0.854901960784314, 0.635294117647059, 0.462745098039216], [0.580392156862745, 0.588235294117647, 0.305882352941176], [0.0666666666666667, 0.717647058823529, 0.0901960784313725], [0.133333333333333, 0.627450980392157, 0.0862745098039216], [0.16078431372549, 0.415686274509804, 0.125490196078431], [0.219607843137255, 0.52156862745098, 0.431372549019608], [0.556862745098039, 0.525490196078431, 0.611764705882353], [0.227450980392157, 0.741176470588235, 0.286274509803922], [0.176470588235294, 0.376470588235294, 0.466666666666667], [0.752941176470588, 0.0705882352941176, 0.541176470588235], [0.572549019607843, 0.227450980392157, 0.588235294117647], [0.862745098039216, 0.549019607843137, 0.654901960784314], [0.945098039215686, 0.490196078431373, 0.274509803921569], [0.749019607843137, 0.556862745098039, 0.780392156862745], [0.654901960784314, 0.937254901960784, 0.796078431372549], [0.890196078431372, 0.525490196078431, 0.101960784313725], [0.768627450980392, 0.670588235294118, 0.2], [0.905882352941176, 0.443137254901961, 0.654901960784314], [0.43921568627451, 0.56078431372549, 0.0509803921568627], [0.235294117647059, 0.552941176470588, 0.717647058823529], [0.105882352941176, 0.858823529411765, 0.788235294117647], [0.341176470588235, 0.101960784313725, 0.0823529411764706], [0.780392156862745, 0.745098039215686, 0.180392156862745], [0.741176470588235, 0.392156862745098, 0.976470588235294], [0.517647058823529, 0.258823529411765, 1], [0.854901960784314, 0.580392156862745, 0.101960784313725], [0.858823529411765, 0.211764705882353, 0.737254901960784], [0.968627450980392, 0.2, 0.419607843137255], [0.698039215686274, 0.380392156862745, 0.517647058823529], [0.905882352941176, 0.0196078431372549, 0.988235294117647], [0.372549019607843, 0.419607843137255, 0.0470588235294118], [0.945098039215686, 0.635294117647059, 0.27843137254902], [0.47843137254902, 0.427450980392157, 0.729411764705882], [0.188235294117647, 0.976470588235294, 0.172549019607843], [0.309803921568627, 0.662745098039216, 0.56078431372549], [0.745098039215686, 0.0196078431372549, 0.215686274509804], [0.00784313725490196, 0.0313725490196078, 0.345098039215686], [0.341176470588235, 0.411764705882353, 0.850980392156863], [0.611764705882353, 0.254901960784314, 0.52156862745098], [0.266666666666667, 0.858823529411765, 0.925490196078431], [0.980392156862745, 0.647058823529412, 0.933333333333333], [0.447058823529412, 0.137254901960784, 0.635294117647059], [0.333333333333333, 0.603921568627451, 0.890196078431372], [0.670588235294118, 0.909803921568627, 0.462745098039216], [0.831372549019608, 0.16078431372549, 0.619607843137255], [0.192156862745098, 0.0784313725490196, 0.231372549019608], [0.713725490196078, 0.529411764705882, 0.976470588235294], [0.203921568627451, 0.749019607843137, 0.980392156862745], [0.87843137254902, 0.368627450980392, 0.356862745098039], [0.913725490196078, 0.952941176470588, 0.498039215686275], [0.0666666666666667, 0.929411764705882, 0.266666666666667], [0.784313725490196, 0.486274509803922, 0.152941176470588], [0.117647058823529, 0.741176470588235, 0.894117647058824], [0.776470588235294, 0.466666666666667, 0.533333333333333], [0.133333333333333, 0.254901960784314, 0.494117647058824], [0.635294117647059, 0.294117647058824, 0.882352941176471], [0.627450980392157, 0.470588235294118, 0.47843137254902], [0.592156862745098, 0.815686274509804, 0.145098039215686], [0.290196078431373, 0.333333333333333, 0.262745098039216], [0.0392156862745098, 0.552941176470588, 0.847058823529412], [0.725490196078431, 0.835294117647059, 0.164705882352941], [0.619607843137255, 0.882352941176471, 0.945098039215686], [0.36078431372549, 0.807843137254902, 0.0823529411764706], [0.498039215686275, 0.941176470588235, 0.509803921568627], [0.572549019607843, 0.733333333333333, 0.72156862745098], [0.0745098039215686, 0.113725490196078, 0.623529411764706], [0.533333333333333, 0.325490196078431, 0.00784313725490196], [0.752941176470588, 0.811764705882353, 0.917647058823529], [0.180392156862745, 0.2, 0.941176470588235], [0.27843137254902, 0.909803921568627, 0.156862745098039], [0.133333333333333, 0.650980392156863, 0.258823529411765], [0.250980392156863, 0.588235294117647, 0.435294117647059], [0.898039215686275, 0.729411764705882, 0.294117647058824], [0.631372549019608, 0.0392156862745098, 0.717647058823529], [0.564705882352941, 0.392156862745098, 0.541176470588235], [0.317647058823529, 0.596078431372549, 0.4], [0.133333333333333, 0.274509803921569, 0.474509803921569], [0.749019607843137, 0.164705882352941, 0.713725490196078], [0.305882352941176, 0.749019607843137, 0.533333333333333], [0.145098039215686, 0.647058823529412, 0.356862745098039], [0.925490196078431, 0.243137254901961, 0.972549019607843], [0.890196078431372, 0.286274509803922, 0.749019607843137], [0.227450980392157, 0.509803921568627, 0.929411764705882], [0.823529411764706, 0.686274509803922, 0.631372549019608], [0.0901960784313725, 0.125490196078431, 0.113725490196078], [0.423529411764706, 0.498039215686275, 0.266666666666667], [0.780392156862745, 0.682352941176471, 0.176470588235294], [0.00392156862745098, 0.682352941176471, 0.607843137254902], [0.725490196078431, 0.556862745098039, 0.215686274509804], [0.443137254901961, 0.0666666666666667, 0.823529411764706], [0.713725490196078, 0.980392156862745, 0.882352941176471], [0.988235294117647, 0.36078431372549, 0.83921568627451], [0.486274509803922, 0.792156862745098, 0.235294117647059], [0.482352941176471, 0.603921568627451, 0.447058823529412], [0.592156862745098, 0.152941176470588, 0.733333333333333]]
# data classes (0 = black, 1 = white)
Y = [0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1]
logreg = LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial')
# fit model to training data and classes
logreg.fit(X, Y)
# please deactivate prediction display to prevent placebo effect during experiment
if len(points) > 1:
if logreg.predict(z.reshape(1,-1)) == 1:
display.blit(predPic, (width / 3 - 80 / 2, 200))
elif logreg.predict(z.reshape(1,-1)) == 0:
display.blit(predPic, (width / 1.5 - 80 / 2, 200))
# update display
pygame.display.update()
text('Correct Predictions: '+str(np.sum(corcnt)-2), (255,255,255), smallfont, width / 1.5 - -135 / 2, 230)
text('Ratio: '+str((np.sum(corcnt)-2)/(number-2)), (255,255,255), smallfont, width / 1.5 - -135 / 2, 190)
loop = True
while loop:
# check for input to exit
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
quit()
# if prediction correct, append array with integer val. 1 to array of correct predictions
if event.type == pygame.KEYDOWN:
if (event.key == pygame.K_LEFT and logreg.predict(z.reshape(1,-1)) == 1):
corcnt = np.vstack((np.array([1]), corcnt))
loop = False
elif (event.key == pygame.K_RIGHT and logreg.predict(z.reshape(1,-1)) == 0):
corcnt = np.vstack((np.array([1]), corcnt))
loop = False
elif event.key == pygame.K_LEFT: # if prediction incorrect, append to array of incorrect predictions
incnt = np.vstack((np.array([1]), incnt))
loop = False
elif event.key == pygame.K_RIGHT:
incnt = np.vstack((np.array([1]), incnt))
loop = False
# check if button presses are down or up, if they are, they save or import data
if event.key == pygame.K_DOWN:
# check if previous file exists
try:
os.remove("Prediction Data (Logistic Regression).txt")
except:
pass
# create a file for prediction data
stringToFile('Correct Predictions: '+str(np.sum(corcnt)-2)+'\nTotal Predictions: '+str(number-2)+'\nRatio: '+str((np.sum(corcnt)-2)/(np.sum(incnt)+np.sum(corcnt)-3)), "Prediction Data (Logistic Regression).txt")
text("Data Successfully Saved to File", (255,255,255), smallfont, 10, 10)
pygame.display.update()
if event.key == pygame.K_TAB:
x = np.asarray(X)
y = np.asarray(Y)
fig = pyplot.figure()
ax = Axes3D(fig)
ax.set_facecolor((1.0, 0.47, 0.42))
for n in range(len(x)):
if y[n] == 0:
ax.scatter(x[n,0], x[n,1], x[n,2], c="black")
elif y[n] == 1:
ax.scatter(x[n,0], x[n,1], x[n,2], c="white")
ax.set_xlabel('R', fontsize = 15)
ax.set_ylabel('B', fontsize = 15)
ax.set_zlabel('G', fontsize = 15)
pyplot.show()
main (nc - prediction only).py
+ 8
− 8
View file @ 7e06a89e
......@@ -36,17 +36,17 @@ downPic = pygame.image.load('down.png')
pygame.display.set_icon(iconPic)
# title of app
pygame.display.set_caption('Nearest Centroid Text Color Chooser 2 - Prediction')
pygame.display.set_caption('Nearest Centroid Text Color Chooser 3 - Prediction')
# set fonts
smallfont = pygame.font.SysFont('comicsansms', 25)
mediumfont = pygame.font.SysFont('comicsansms', 50)
largefont = pygame.font.SysFont('comicsansms', 80)
# matrixes of color, index 0 should be deleted
points = np.reshape(np.array([randint(0,255), randint(0,255), randint(0,255)]), (-1, 3))
white = np.reshape(np.array([randint(0,255), randint(0,255), randint(0,255)]), (-1, 3))
black = np.reshape(np.array([randint(0,255), randint(0,255), randint(0,255)]), (-1, 3))
# white text looks best on black background and reversed
points = np.reshape(np.array([[0, 0, 0],[255,255,255]]), (-1, 3))
white = np.reshape(np.array([0, 0, 0]), (-1, 3))
black = np.reshape(np.array([255, 255, 255]), (-1, 3))
# initial arrays for correct and total predictions for vstack
corcnt = np.array([1])
......@@ -98,8 +98,8 @@ while True:
blackcent = centroid(black)
y = np.reshape(color, (-1, 3))
# deactivated prediction display to prevent placebo effect during experiment
"""
# please deactivate prediction display to prevent placebo effect during experiment
if len(points) > 1:
if scipy.spatial.distance.euclidean(y, whitecent) < scipy.spatial.distance.euclidean(y, blackcent):
display.blit(predPic, (width / 3 - 80 / 2, 200))
......@@ -108,7 +108,7 @@ while True:
else:
display.blit(predPic, (width / 1.5 - 80 / 2, 200))
display.blit(predPic, (width / 3 - 80 / 2, 200))
"""
# update display
pygame.display.update()
......
main (neural network - prediction only).py 0 → 100644
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View file @ 7e06a89e
# -*- coding: utf-8 -*-
# Neural Network-powered Text Color Chooser - Prediction Only: Main
import pygame
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import pyplot
from mpl_toolkits.mplot3d import Axes3D
from random import randint
from strToFile import stringToFile
from random import seed
from sklearn.neural_network import MLPClassifier
# init
pygame.init()
# resolution
width = 800
height = 600
# make the pygame window
display = pygame.display.set_mode((width, height))
# set framerate
clock = pygame.time.Clock()
clock.tick(60)
# keyboard pictures and predictor
leftPic = pygame.image.load('left.png')
rightPic = pygame.image.load('right.png')
predPic = pygame.image.load('predictor.png')
iconPic = pygame.image.load('icon.png')
downPic = pygame.image.load('down.png')
# set window icon
pygame.display.set_icon(iconPic)
# title of app
pygame.display.set_caption('Neural Network-powered Text Color Chooser v. 1.2 - Prediction - Training Data Sample Size: 384')
# set fonts
smallfont = pygame.font.SysFont('comicsansms', 25)
mediumfont = pygame.font.SysFont('comicsansms', 50)
largefont = pygame.font.SysFont('comicsansms', 80)
# matrixes of color, index 0 should be deleted
points = np.reshape(np.array([[0, 0, 0],[255,255,255]]), (-1, 3))
white = np.reshape(np.array([0, 0, 0]), (-1, 3))
black = np.reshape(np.array([255, 255, 255]), (-1, 3))
# initial arrays for correct and total predictions for vstack
corcnt = np.array([1])
incnt = np.array([1])
# ////////////////////////////////////////////////////////////
# write on screen
# ////////////////////////////////////////////////////////////
def text(msg,color,font,x,y):
screen_text = font.render(msg, True, color)
display.blit(screen_text, [x, y])
# program loop
number = 0
while True:
# random color
color = np.array([randint(0,255), randint(0,255), randint(0,255)])
number += 1
# color background
display.fill((123, 123, 123))
# display information
text('Total Predictions: '+str(number-2), (255,255,255), smallfont, width / 1.5 - -135 / 2, 210)
# draw color boxes
pygame.draw.rect(display, color, [width / 3 - 200 / 2, 300, 200, 200])
pygame.draw.rect(display, color, [width / 1.5 - 200 / 2, 300, 200, 200])
pygame.draw.rect(display, (37,37,37), [width / 3 - 220 / 2 - 4, 75, 220, 40])
# blit pics of arrows
display.blit(leftPic, (width / 3 - 63 / 2, 512))
display.blit(rightPic, (width / 1.5 - 63 / 2, 512))
display.blit(downPic, ((width / 3 - 63 / 2), 125))
# text
text('Text', (255,255,255), largefont, width / 3 - 200 / 2 + 20, 330)
text('Text', (0,0,0), largefont, width / 1.5 - 200 / 2 + 20, 330)
text('Save Prediction Data', (255,255,255), smallfont, width / 3 - 216 / 2, 75)
text('RGB Color Value: ',(255,255,255), smallfont, 5, 200)
text(str(color), (255,255,255), smallfont, 5, 230)
text('press tab to show data', (255,255,255), smallfont, width - 275, 10)
# convert RGB values to between 0 and 1
z = color/255
# training data (sample size: 384)
X = [[0.729411764705882, 0.647058823529412, 0.945098039215686], [0.482352941176471, 0.443137254901961, 0.0862745098039216], [0.643137254901961, 0.525490196078431, 0.313725490196078], [0.462745098039216, 0.933333333333333, 0.501960784313725], [0.588235294117647, 0.168627450980392, 0.768627450980392], [0.854901960784314, 0.635294117647059, 0.462745098039216], [0.580392156862745, 0.588235294117647, 0.305882352941176], [0.0666666666666667, 0.717647058823529, 0.0901960784313725], [0.133333333333333, 0.627450980392157, 0.0862745098039216], [0.16078431372549, 0.415686274509804, 0.125490196078431], [0.219607843137255, 0.52156862745098, 0.431372549019608], [0.556862745098039, 0.525490196078431, 0.611764705882353], [0.227450980392157, 0.741176470588235, 0.286274509803922], [0.176470588235294, 0.376470588235294, 0.466666666666667], [0.752941176470588, 0.0705882352941176, 0.541176470588235], [0.572549019607843, 0.227450980392157, 0.588235294117647], [0.862745098039216, 0.549019607843137, 0.654901960784314], [0.945098039215686, 0.490196078431373, 0.274509803921569], [0.749019607843137, 0.556862745098039, 0.780392156862745], [0.654901960784314, 0.937254901960784, 0.796078431372549], [0.890196078431372, 0.525490196078431, 0.101960784313725], [0.768627450980392, 0.670588235294118, 0.2], [0.905882352941176, 0.443137254901961, 0.654901960784314], [0.43921568627451, 0.56078431372549, 0.0509803921568627], [0.235294117647059, 0.552941176470588, 0.717647058823529], [0.105882352941176, 0.858823529411765, 0.788235294117647], [0.341176470588235, 0.101960784313725, 0.0823529411764706], [0.780392156862745, 0.745098039215686, 0.180392156862745], [0.741176470588235, 0.392156862745098, 0.976470588235294], [0.517647058823529, 0.258823529411765, 1], [0.854901960784314, 0.580392156862745, 0.101960784313725], [0.858823529411765, 0.211764705882353, 0.737254901960784], [0.968627450980392, 0.2, 0.419607843137255], [0.698039215686274, 0.380392156862745, 0.517647058823529], [0.905882352941176, 0.0196078431372549, 0.988235294117647], [0.372549019607843, 0.419607843137255, 0.0470588235294118], [0.945098039215686, 0.635294117647059, 0.27843137254902], [0.47843137254902, 0.427450980392157, 0.729411764705882], [0.188235294117647, 0.976470588235294, 0.172549019607843], [0.309803921568627, 0.662745098039216, 0.56078431372549], [0.745098039215686, 0.0196078431372549, 0.215686274509804], [0.00784313725490196, 0.0313725490196078, 0.345098039215686], [0.341176470588235, 0.411764705882353, 0.850980392156863], [0.611764705882353, 0.254901960784314, 0.52156862745098], [0.266666666666667, 0.858823529411765, 0.925490196078431], [0.980392156862745, 0.647058823529412, 0.933333333333333], [0.447058823529412, 0.137254901960784, 0.635294117647059], [0.333333333333333, 0.603921568627451, 0.890196078431372], [0.670588235294118, 0.909803921568627, 0.462745098039216], [0.831372549019608, 0.16078431372549, 0.619607843137255], [0.192156862745098, 0.0784313725490196, 0.231372549019608], [0.713725490196078, 0.529411764705882, 0.976470588235294], [0.203921568627451, 0.749019607843137, 0.980392156862745], [0.87843137254902, 0.368627450980392, 0.356862745098039], [0.913725490196078, 0.952941176470588, 0.498039215686275], [0.0666666666666667, 0.929411764705882, 0.266666666666667], [0.784313725490196, 0.486274509803922, 0.152941176470588], [0.117647058823529, 0.741176470588235, 0.894117647058824], [0.776470588235294, 0.466666666666667, 0.533333333333333], [0.133333333333333, 0.254901960784314, 0.494117647058824], [0.635294117647059, 0.294117647058824, 0.882352941176471], [0.627450980392157, 0.470588235294118, 0.47843137254902], [0.592156862745098, 0.815686274509804, 0.145098039215686], [0.290196078431373, 0.333333333333333, 0.262745098039216], [0.0392156862745098, 0.552941176470588, 0.847058823529412], [0.725490196078431, 0.835294117647059, 0.164705882352941], [0.619607843137255, 0.882352941176471, 0.945098039215686], [0.36078431372549, 0.807843137254902, 0.0823529411764706], [0.498039215686275, 0.941176470588235, 0.509803921568627], [0.572549019607843, 0.733333333333333, 0.72156862745098], [0.0745098039215686, 0.113725490196078, 0.623529411764706], [0.533333333333333, 0.325490196078431, 0.00784313725490196], [0.752941176470588, 0.811764705882353, 0.917647058823529], [0.180392156862745, 0.2, 0.941176470588235], [0.27843137254902, 0.909803921568627, 0.156862745098039], [0.133333333333333, 0.650980392156863, 0.258823529411765], [0.250980392156863, 0.588235294117647, 0.435294117647059], [0.898039215686275, 0.729411764705882, 0.294117647058824], [0.631372549019608, 0.0392156862745098, 0.717647058823529], [0.564705882352941, 0.392156862745098, 0.541176470588235], [0.317647058823529, 0.596078431372549, 0.4], [0.133333333333333, 0.274509803921569, 0.474509803921569], [0.749019607843137, 0.164705882352941, 0.713725490196078], [0.305882352941176, 0.749019607843137, 0.533333333333333], [0.145098039215686, 0.647058823529412, 0.356862745098039], [0.925490196078431, 0.243137254901961, 0.972549019607843], [0.890196078431372, 0.286274509803922, 0.749019607843137], [0.227450980392157, 0.509803921568627, 0.929411764705882], [0.823529411764706, 0.686274509803922, 0.631372549019608], [0.0901960784313725, 0.125490196078431, 0.113725490196078], [0.423529411764706, 0.498039215686275, 0.266666666666667], [0.780392156862745, 0.682352941176471, 0.176470588235294], [0.00392156862745098, 0.682352941176471, 0.607843137254902], [0.725490196078431, 0.556862745098039, 0.215686274509804], [0.443137254901961, 0.0666666666666667, 0.823529411764706], [0.713725490196078, 0.980392156862745, 0.882352941176471], [0.988235294117647, 0.36078431372549, 0.83921568627451], [0.486274509803922, 0.792156862745098, 0.235294117647059], [0.482352941176471, 0.603921568627451, 0.447058823529412], [0.592156862745098, 0.152941176470588, 0.733333333333333]]
# data classes (0 = black, 1 = white)
Y = [0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1]
clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
# fit model to training data and classes
clf.fit(X, Y)
# please deactivate prediction display to prevent placebo effect during experiment
if len(points) > 1:
if clf.predict(z.reshape(1,-1)) == 1:
display.blit(predPic, (width / 3 - 80 / 2, 200))
elif clf.predict(z.reshape(1,-1)) == 0:
display.blit(predPic, (width / 1.5 - 80 / 2, 200))
# update display
pygame.display.update()
text('Correct Predictions: '+str(np.sum(corcnt)-2), (255,255,255), smallfont, width / 1.5 - -135 / 2, 230)
text('Ratio: '+str((np.sum(corcnt)-2)/(number-2)), (255,255,255), smallfont, width / 1.5 - -135 / 2, 190)
loop = True
while loop:
# check for input to exit
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
quit()
# if prediction correct, append array with integer val. 1 to array of correct predictions
if event.type == pygame.KEYDOWN:
if (event.key == pygame.K_LEFT and clf.predict(z.reshape(1,-1)) == 1):
corcnt = np.vstack((np.array([1]), corcnt))
loop = False
elif (event.key == pygame.K_RIGHT and clf.predict(z.reshape(1,-1)) == 0):
corcnt = np.vstack((np.array([1]), corcnt))
loop = False
elif event.key == pygame.K_LEFT: # if prediction incorrect, append to array of incorrect predictions
incnt = np.vstack((np.array([1]), incnt))
loop = False
elif event.key == pygame.K_RIGHT:
incnt = np.vstack((np.array([1]), incnt))
loop = False
# check if button presses are down or up, if they are, they save or import data
if event.key == pygame.K_DOWN:
# check if previous file exists
try:
os.remove("Prediction Data (Neural Network).txt")
except:
pass
# create a file for prediction data
stringToFile('Correct Predictions: '+str(np.sum(corcnt)-2)+'\nTotal Predictions: '+str(number-2)+'\nRatio: '+str((np.sum(corcnt)-2)/(np.sum(incnt)+np.sum(corcnt)-3)), "Prediction Data (Neural Network).txt")
text("Data Successfully Saved to File", (255,255,255), smallfont, 10, 10)
pygame.display.update()
if event.key == pygame.K_TAB:
x = np.asarray(X)
y = np.asarray(Y)
fig = pyplot.figure()
ax = Axes3D(fig)
ax.set_facecolor((1.0, 0.47, 0.42))
for n in range(len(x)):
if y[n] == 0:
ax.scatter(x[n,0], x[n,1], x[n,2], c="black")
elif y[n] == 1:
ax.scatter(x[n,0], x[n,1], x[n,2], c="white")
ax.set_xlabel('R', fontsize = 15)
ax.set_ylabel('B', fontsize = 15)
ax.set_zlabel('G', fontsize = 15)
pyplot.show()
\ No newline at end of file
main (nn - prediction only).py
+ 8
− 8
View file @ 7e06a89e
......@@ -36,17 +36,17 @@ downPic = pygame.image.load('down.png')
pygame.display.set_icon(iconPic)
# title of app
pygame.display.set_caption('Nearest Neighbor Text Color Chooser 2 - Prediction')
pygame.display.set_caption('Nearest Neighbor Text Color Chooser 2.5 - Prediction')
# set fonts
smallfont = pygame.font.SysFont('comicsansms', 25)
mediumfont = pygame.font.SysFont('comicsansms', 50)
largefont = pygame.font.SysFont('comicsansms', 80)
# matrixes of color, index 0 should be deleted
points = np.array([[1000000,1000000,1000000]])
white = np.array([[1000000,1000000,1000000]])
black = np.array([[1000000,1000000,1000000]])
# white text looks best on black background and reversed
points = np.reshape(np.array([[0, 0, 0],[255,255,255]]), (-1, 3))
white = np.reshape(np.array([0, 0, 0]), (-1, 3))
black = np.reshape(np.array([255, 255, 255]), (-1, 3))
# initial arrays for correct and total predictions for vstack
corcnt = np.array([1])
......@@ -97,8 +97,8 @@ while True:
y = np.reshape(color, (-1, 3))
prediction = nn(points, y)
# deactivated prediction display to prevent placebo effect during experiment
"""
# please deactivate prediction display to prevent placebo effect during experiment
if len(points) > 1:
for n in white:
if np.array_equal(n, prediction):
......@@ -106,7 +106,7 @@ while True:
for n in black:
if np.array_equal(n, prediction):
display.blit(predPic, (width / 1.5 - 80 / 2, 200))
"""
# update display
pygame.display.update()
......
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