hackintoshrao

# -*- coding:utf8 -*-
# !/usr/bin/env python
# Copyright 2017 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#

hackintoshrao

{
  "id": "035bea35-c51b-4ccc-be8c-61846b506b78",
  "timestamp": "2018-01-26T14:04:28.967Z",
  "lang": "en",
  "result": {
    "source": "agent",
    "resolvedQuery": "I don't how to update goals",
    "action": "",
    "actionIncomplete": false,
    "parameters": {

hackintoshrao

satisfaction_level	last_evaluation	number_project	average_montly_hours	time_spend_company	Work_accident	left	promotion_last_5years	sales	salary
0.38	0.53	2	157	3	0	1	0	sales	low
0.8	0.86	5	262	6	0	1	0	sales	medium
0.11	0.88	7	272	4	0	1	0	sales	medium
0.72	0.87	5	223	5	0	1	0	sales	low
0.37	0.52	2	159	3	0	1	0	sales	low
0.41	0.5	2	153	3	0	1	0	sales	low
0.1	0.77	6	247	4	0	1	0	sales	low
0.92	0.85	5	259	5	0	1	0	sales	low
0.89	1	5	224	5	0	1	0	sales	low

hackintoshrao

import pandas as pd


weight1 = 0.0
weight2 = -2.0
bias = 1.0


# DON'T CHANGE ANYTHING BELOW
# Inputs and outputs

hackintoshrao

import pandas as pd

# Set weight1, weight2, and bias so that only the inputs 1,1 falls into positive area 
weight1 = 2.0
weight2 = 2.0
bias = -1


# Inputs and otutputs
test_inputs = [(0, 0), (0, 1), (1, 0), (1, 1)]

hackintoshrao

import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob

# prepare object points
#Enter the number of inside corners in x
nx = 9
#Enter the number of inside corners in y

hackintoshrao

# -----------
# User Instructions
#
# Implement a P controller by running 100 iterations
# of robot motion. The desired trajectory for the 
# robot is the x-axis. The steering angle should be set
# by the parameter tau so that:
#
# steering = -tau * crosstrack_error
#

hackintoshrao

import gym

# Use the cartpole environment.
env = gym.make('CartPole-v0')
# environment has to be reset first.
env.reset()
# This flag to is used to represent the end of an episode.
# An episode ends when the pole falls down.
done = False
# counter to check the number of moves for which the balance of the pole 

hackintoshrao

import math
import rospy
from std_msgs.msg import Float64

def mover():
    pub_j1 = rospy.Publisher('/simple_arm/joint_1_position_controller/command',
                             Float64, queue_size=10)
    pub_j2 = rospy.Publisher('/simple_arm/joint_2_position_controller/command',
                             Float64, queue_size=10)
    rospy.init_node('arm_mover')

hackintoshrao

def get_embedding_layer(vocab_index, learned_embeddings, max_words, embedding_dim, max_seq_len, do_train):
        """"
        Creates the Embedding layer using learned embedding and word indesx map of all
        unique words in the text corpus.

        ||Params||

        vocab_index : Map of all unique words in your text corpus as keys and their index as values
        learned_embeddings: Learned embedding representation from GLove/Word2Vec.
        max_words: Max words to be used from the vocab