coreyauger

Keybase proof

I hereby claim:

• I am coreyauger on github.
• I am coreyauger (https://keybase.io/coreyauger) on keybase.
• I have a public key ASDY8Z2DcU9q-n5nykzxsJyAq0fQ61GvnnTFvQKF8j7j6wo

To claim this, I am signing this object:

coreyauger

#include <SDL2/SDL.h>
#include <iostream>
using namespace std;

const int SCREEN_WIDTH = 800;
const int SCREEN_HEIGHT = 600;

int main( int argc, char* args[] ){
	SDL_Window* window = NULL;
	SDL_Surface* screenSurface = NULL;

coreyauger

android {
    compileSdkVersion 26
    buildToolsVersion "26.0.1"
    defaultConfig {
        if (buildAsApplication) {
            applicationId "io.surfkit.simple"
        }
        minSdkVersion 24
        targetSdkVersion 26
        versionCode 1

coreyauger

android {
    compileSdkVersion 26
    buildToolsVersion "26.0.1"
    defaultConfig {
        if (buildAsApplication) {
            applicationId "io.surfkit"
        }
        minSdkVersion 24
        targetSdkVersion 26

coreyauger

#include <exception>
#include <functional>

#define GL_GLEXT_PROTOTYPES 1
#include <SDL2/SDL.h>
#include <SDL2/SDL_opengles2.h>

// Shader sources
const GLchar* vertexSource =

coreyauger

import numpy as np
import os
import dtdata as dt
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Input
from keras.regularizers import l1
from keras.optimizers import RMSprop, Adam

coreyauger

#################################################################################################
## TRAIN FUTURE ENCODER
#################################################################################################
x_train_future = data[hold_out:,20:2420]
print("training on: " + str(x_train_future.shape))

input_future = Input(shape=(x_train_future.shape[1],))
encoded_future = Dense(encoding_dim, activation='relu')(input_future)
decoded_future = Dense(x_train_future.shape[1], activation='linear')(encoded_future)
autoencoder_future = Model(input_future, decoded_future)

coreyauger

#################################################################################################
## TRAIN PAST ENCODER
#################################################################################################
x_train_past = data[hold_out:,0:2400]
print("training past on: " + str(x_train_past.shape))

input_past = Input(shape=(x_train_past.shape[1],))
encoded_past = Dense(encoding_dim, activation='relu')(input_past)
decoded_past = Dense(x_train_past.shape[1], activation='linear')(encoded_past)
autoencoder_past = Model(input_past, decoded_past)

coreyauger

import numpy as np
import os
import dtdata as dt
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Input
from keras.regularizers import l1
from keras.optimizers import RMSprop, Adam

coreyauger

import numpy as np
import os
import dtdata as dt

import matplotlib.pyplot as plt
import math
import random
import pprint as pp
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, StandardScaler