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fragment float4 capturedImageFragmentShader(ImageColorInOut in [[stage_in]],
                                            texture2d<float, access::sample> capturedImageTextureY [[ texture(kTextureIndexY) ]],
                                            texture2d<float, access::sample> capturedImageTextureCbCr [[ texture(kTextureIndexCbCr) ]]) {
    
    constexpr sampler colorSampler(mip_filter::linear,
                                   mag_filter::linear,
                                   min_filter::linear);
    
    const float4x4 ycbcrToRGBTransform = float4x4(
        float4(+1.0000f, +1.0000f, +1.0000f, +0.0000f),

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#include "std_try.h"
#include <math.h>

using namespace Halide;

#define PI 3.14159
/*
  Interpolations
*/

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//: Simple Genetic Algorithm Starter in Swift 3

import UIKit
import Foundation

let AVAILABLE_GENES:[Int] = Array(1...100)
let DNA_LENGTH = 6

let TOURNAMENT_SIZE = 5
let MAX_GENERATIONS_COUNT = 100

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import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, n_layers=1):
        super(RNN, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size

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/*
  The "hello world" of neural networks: a simple 3-layer feed-forward
  network that implements an XOR logic gate.

  The first layer is the input layer. It has two neurons a and b, which
  are the two inputs to the XOR gate.

  The middle layer is the hidden layer. This has two neurons h1, h2 that
  will learn what it means to be an XOR gate.
 

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#!/bin/bash

echo "Building pjsip:"

# change this to whatever DEVPATH works
# if you get make errors, maybe redownload pjsip and try again

export DEVPATH=/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneSimulator.platform/Developer

MIN_IOS="-miphoneos-version-min=9.0" ARCH="-arch i386" CFLAGS="-O2 -m32 -mios-simulator-version-min=9.0 -fembed-bitcode" LDFLAGS="-O2 -m32 -mios-simulator-version-min=9.0 -fembed-bitcode" ./configure-iphone

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    ACTION = build
    AD_HOC_CODE_SIGNING_ALLOWED = NO
    ALTERNATE_GROUP = staff
    ALTERNATE_MODE = u+w,go-w,a+rX
    ALTERNATE_OWNER = grantdavis
    ALWAYS_SEARCH_USER_PATHS = NO
    ALWAYS_USE_SEPARATE_HEADERMAPS = YES
    APPLE_INTERNAL_DEVELOPER_DIR = /AppleInternal/Developer
    APPLE_INTERNAL_DIR = /AppleInternal
    APPLE_INTERNAL_DOCUMENTATION_DIR = /AppleInternal/Documentation

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# use pkg-config for getting CFLAGS and LDLIBS
FFMPEG_LIBS=    libavdevice                        \
                libavformat                        \
                libavfilter                        \
                libavcodec                         \
                libswresample                      \
                libswscale                         \
                libavutil                          \

CFLAGS += -Wall -g

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import keras.backend as K
import tensorflow as tf
class Model:
  def __init__(self,batch_size):
    self.batch_size = batch_size
    
    def loss_DSSIS_tf11(self, y_true, y_pred):
        """Need tf0.11rc to work"""
        y_true = tf.reshape(y_true, [self.batch_size] + get_shape(y_pred)[1:])
        y_pred = tf.reshape(y_pred, [self.batch_size] + get_shape(y_pred)[1:])

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# Builds a Libjpeg framework for the iPhone and the iPhone Simulator.
# Creates a set of universal libraries that can be used on an iPhone and in the
# iPhone simulator. Then creates a pseudo-framework to make using libjpeg in Xcode
# less painful.
#
# To configure the script, define:
#    IPHONE_SDKVERSION: iPhone SDK version (e.g. 8.1)
#
# Then go get the source tar.bz of the libjpeg you want to build, shove it in the
# same directory as this script, and run "./libjpeg.sh". Grab a cuppa. And voila.