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/*==============================================================================
Copyright (c) 2017 PTC Inc. All Rights Reserved.

Copyright (c) 2010-2014 Qualcomm Connected Experiences, Inc.
All Rights Reserved.
Confidential and Proprietary - Protected under copyright and other laws.
==============================================================================*/

using UnityEngine;
using Vuforia;

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using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class RotateEarth : MonoBehaviour {

	// Use this for initialization
	void Start () {
		
	}
	

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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

"""Minimal script for generating an image using pre-trained StyleGAN generator."""

import os

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!git clone https://github.com/NVlabs/stylegan.git

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import sys
sys.path.insert(0, "/content/stylegan")

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from IPython.display import Image
Image('/content/example.png')

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# تحميل مكتبة الباندا والتنسرفلو
import pandas as pd
import tensorflow as tf
# طباعة رقم نسخة التنسرفلر
print(tf.__version__)

#downlad data from GCS and store as pandas dataframe 
# تحميل البيانات (التديب والاختبار) من الروابط  
data_train = pd.read_csv(
  filepath_or_buffer='https://storage.googleapis.com/vijay-public/boston_housing/housing_train.csv',

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FEATURES = ["CRIM", "ZN", "INDUS", "NOX", "RM",
            "AGE", "DIS", "TAX", "PTRATIO"]# المميزات وهي الصفات المختارة من الأعمدة
LABEL = "MEDV"  #  المسمى الذي نرغب ان يتوقعه النموذح وهنا هو القيمة المتوسطة للمنازل

feature_cols = [tf.feature_column.numeric_column(k)
                  for k in FEATURES] #list of Feature Columns  

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def generate_estimator(output_dir):
  return tf.estimator.DNNRegressor(feature_columns=feature_cols,
                                            hidden_units=[10, 10],
                                            model_dir=output_dir)

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def generate_input_fn(data_set):
    def input_fn():
      features = {k: tf.constant(data_set[k].values) for k in FEATURES}
      labels = tf.constant(data_set[LABEL].values)
      return features, labels
    return input_fn