miquelbeltran

abstract class Beverage {
    open val description: String = "Unknown Beverage"
    abstract val cost: Double
}

abstract class CondimentDecorator : Beverage() {
    abstract override val description: String
}

class Espresso: Beverage() {

miquelbeltran

import "dart:async";

class SynchronousError<T> implements Future<T> {
  final Object _error;

  SynchronousError(this._error);

  @override
  Stream<T> asStream() {
    final StreamController controller = StreamController<T>();

miquelbeltran

#!/bin/sh
# To use add to `.git/hooks/`
# Should be named `pre-push`
# Make executable with `chmod +x`

# stash any unstaged changes
git stash -q --keep-index

# run Flutter analyze + test
flutter analyze

miquelbeltran

package com.miquelbeltran.accordion;

import android.app.Activity;
import android.os.Bundle;
import android.view.Menu;
import android.view.MenuItem;
import android.widget.TextView;

public class Accordion extends Activity {
    private static final int MENU_NEW_GAME = 1;

miquelbeltran

 <!-- Used for Google Play Store Campaign Measurement -->
       <receiver
android:name=“.broadcastreceivers.CampaignTrackingReceiver”
           android:enabled=“true”
           android:exported=“true”>
           <intent-filter>
               <action android:name=“com.android.vending.INSTALL_REFERRER” />
           </intent-filter>
       </receiver>

miquelbeltran

fun provideGetCollectionUseCase(): GetCollectionUseCase {
    return GetCollectionUseCaseImpl(provideService())
}

miquelbeltran

val collectionKoinModule = module {
    single { provideGetCollectionUseCase() }
    viewModel { CollectionViewModel(get()) }
}

miquelbeltran

// ML Kit Input
val inp = arrayOf(FloatArray(784) { 0f })

// Read our Bitmap
val bitmap = BitmapFactory.decodeStream(assets.open("three.bmp"))

// Read pixels
val intValues = IntArray(784)
bitmap.getPixels(intValues, 0, bitmap.width, 0, 0, bitmap.width, bitmap.height)

miquelbeltran

// Input is a 1x784 Tensor
val inputDims = intArrayOf(1, 784)
// Output is a 1x10 Tensor
val outputDims = intArrayOf(1, 10)

miquelbeltran

from keras import backend as K

custom_input_tensor = tf.placeholder(tf.float32, shape=(1, 784))
output_tensor = model(custom_input_tensor)

frozen_graph = freeze_session(K.get_session(), output_names[output_tensor.op.name])

tflite_model = tf.contrib.lite.toco_convert(frozen_graph,[custom_input_tensor], [output_tensor])
open("app/src/main/assets/nmist_mlp.tflite", "wb").write(tflite_model)