PonDad

import grove_gesture_sensor
from grove_rgb_lcd import *
import time

def main():
    g=grove_gesture_sensor.gesture()
    g.init()
    setText("-\nNewtral")
    setRGB(255,255,255) #White
    while True:

PonDad

import grove_gesture_sensor
from grove_rgb_lcd import *
import time
import Adafruit_PCA9685
import grove_i2c_motor_driver

pwm = Adafruit_PCA9685.PCA9685()
pwm.set_pwm_freq(60)

m= grove_i2c_motor_driver.motor_driver()

PonDad

# Copyright (c) 2016 Adafruit Industries
# Author: Tony DiCola
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#

PonDad

# -*- coding: utf-8 -*-
"""
Created on Thu May  5, 2016
@author: jonki
https://github.com/jojonki/reinforcement-practice/blob/master/simple-q-learning.py
こちらをPython3で動作する様に一部改変しました
"""

import numpy as np
import random

PonDad

import numpy as np
import pandas as pd


class QLearning:
    def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9):
        # actions = [0, 1, 2, 3]
        self.actions = actions
        self.alpha = learning_rate
        self.discount_factor = reward_decay

PonDad

# -*- coding:utf-8 -*-
from bottle import route, run
import irm
import os

@route('/light_on')
def light_on():
    irm.playIR("room_light_on.json")
    irm.playIR("stand_light_on.json")
    print("Light On!")

PonDad

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils

root_dir = "./image/"
categories = ["red_apple", "green_apple"]
nb_classes = len(categories)
image_size = 32

PonDad

'use strict';

process.env.DEBUG = 'actions-on-google:*';
const App = require('actions-on-google').ApiAiApp;

const exec = require('child_process').exec;
const camera_adress = "https://7bdaf661.ap.ngrok.io/camera"
const what_adress = "https://7bdaf661.ap.ngrok.io/what"
const face_adress = "https://7bdaf661.ap.ngrok.io/face"
const who_adress = "https://7bdaf661.ap.ngrok.io/who"

PonDad

'use strict';

process.env.DEBUG = 'actions-on-google:*';
const App = require('actions-on-google').ApiAiApp;

const exec = require('child_process').exec;
const kinako_adress = "https://xxxxxxxx.ap.ngrok.io/kinako_today"
const anko_adress = "https://xxxxxxxx.ap.ngrok.io/anko_today"
const kinako_adress_2 = "https://xxxxxxxx.ap.ngrok.io/kinako_month"
const anko_adress_2 = "https://xxxxxxxx.ap.ngrok.io/anko_month"

PonDad

#! /usr/bin/env python3
from mvnc import mvncapi as mvnc
import sys
import numpy
import cv2

path_to_networks = './'
path_to_images = '../../data/images/'
graph_filename = 'graph'
#image_filename = path_to_images + 'cat.jpg'