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Archive for September, 2017

Communication between Angular 4 components through RxJs and Observables

Posted by vivekcek on September 27, 2017

Hi Guys this is a quick post about how we can implement communication between Angular 4 components via RxJs and Observable. This example can be done with other component communication mechanisms like @Input() and Services.

But observable based communication will be useful in large Angular 4 applications.
I am not going to explain the concept in detail. Please google it.

Back to our example.

I have two nested components, AppComponent and HelloCoponent. AppCoponent is the parent/root.
AppComponent display a list of dynamically created items. There is a button inside HelloComponent, When clicking on it new items are added and these new items are reflected in AppComponent.

Have a look at the structure.

Have a look at the code.

AppComponent

import { Component,OnInit} from '@angular/core';
import {MessageService} from './message.service'
import {Subscription} from 'rxjs/Subscription'
@Component({
  selector: 'my-app',
  templateUrl: './app.component.html',
  styleUrls: [ './app.component.css' ]
})
export class AppComponent implements OnInit  {
  
  names:string[];

  constructor(private msgServce:MessageService){

  }

  ngOnInit():void{
    this.msgServce.msgPublisher$.subscribe(data=>{this.names=data.names;
    //this.changeDetectorRef.detectChanges();
    
    });
  }
}

AppComponent.html

<hello ></hello>
<ul>
       <li *ngFor="let name of names">
          {{name}}
       </li>
</ul>

HelloCoponent

import { Component, Input } from '@angular/core';
import {MessageService} from './message.service'
@Component({
  selector: 'hello',
  template: `<button (click)="onAdd()">Add</button>`,
  styles: [`h1 { font-family: Lato; }`]
})
export class HelloComponent  {
  constructor(private msgService:MessageService){

  }

  onAdd(){
    this.msgService.addName("Test");
  }
}

MessageService

import {Injectable} from '@angular/core'
import {Subject} from 'rxjs/Subject'

@Injectable()
export class MessageService{
  constructor(){}
  names:string[]=[];
  private messageSource=new Subject<any>();
  msgPublisher$=this.messageSource.asObservable();

  addName(name:string){
    this.names.push(name)
    this.messageSource.next({names:this.names})
  }
}
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Posted in Angular2 | Tagged: , | Leave a Comment »

Predicting linear regression with Tensorflow and Azure Machine Learning Studio (Comparison ,Gradient descent)

Posted by vivekcek on September 20, 2017

In this post I am trying to evaluate the prediction done by Tensorflow and Azure Machine Learning Studio.
I am using a dataset obtained from Courseera machine learning tutorial. I will provide the dataset at the end of this post.
Here is the predicted value I got from Tensorflow and Azure Machine Learning for the input “8.5172”

Azure

Tensorflow

I used linear regression with Gradient descent optimizer, and below are the values used for epoch and learning rate in Tensorflow and azure ML.
Epoch=1000
Learning rate=0.01

Azure Model and Algorithm settings are given below

Tensorflow code is given below.

import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf

# Parameters
display_step = 50
learning_rate = 0.01
training_epochs = 1000

data = pd.read_csv('ex1data1.txt', names=['population', 'profit'])

X_data = data[['population']]
Y_data = data[['profit']]

n_samples = X_data.shape[0]  # Number of rows

# tf Graph Input
X = tf.placeholder('float', shape=X_data.shape)
Y = tf.placeholder('float', shape=Y_data.shape)

# Set model weights
W = tf.Variable(tf.zeros([1, 1]), name='weight')
b = tf.Variable(tf.zeros(1), name='bias')

# Construct a linear model
pred = tf.add(tf.multiply(X, W), b)

# Mean squared error
# cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
cost = tf.reduce_mean(tf.square(pred-Y)) / 2.0

# Gradient descent
# may try other optimizers like AdadeltaOptimizer, AdagradOptimizer, AdamOptimizer, FtrlOptimizer or RMSPropOptimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)
    cost_value, w_value, b_value = (0.0, 0.0, 0.0)
    for epoch in range(training_epochs):
        # Fit all training data
        _, cost_value, w_value, b_value = sess.run((optimizer, cost, W, b), feed_dict={X: X_data, Y: Y_data})

        # Display logs per epoch step
        if (epoch+1) % display_step == 0:
            print('Epoch:', '%04d' % (epoch+1), 'cost=', '{:.9f}'.format(cost_value), \
                'W=', w_value, 'b=', b_value)

    print ('Optimization Finished!')
    print ('Training cost=', cost_value, 'W=', w_value, 'b=', b_value, '\n')
    print('Evaluation')
    print(w_value*8.5172+b_value)
    # Graphic display
    plt.plot(X_data, Y_data, 'ro', label='Original data')
    plt.plot(X_data, w_value * X_data + b_value, label='Fitted line')
    plt.legend()
    plt.show()

Please use below dataset.

6.1101,17.592
5.5277,9.1302
8.5186,13.662
7.0032,11.854
5.8598,6.8233
8.3829,11.886
7.4764,4.3483
8.5781,12
6.4862,6.5987
5.0546,3.8166
5.7107,3.2522
14.164,15.505
5.734,3.1551
8.4084,7.2258
5.6407,0.71618
5.3794,3.5129
6.3654,5.3048
5.1301,0.56077
6.4296,3.6518
7.0708,5.3893
6.1891,3.1386
20.27,21.767
5.4901,4.263
6.3261,5.1875
5.5649,3.0825
18.945,22.638
12.828,13.501
10.957,7.0467
13.176,14.692
22.203,24.147
5.2524,-1.22
6.5894,5.9966
9.2482,12.134
5.8918,1.8495
8.2111,6.5426
7.9334,4.5623
8.0959,4.1164
5.6063,3.3928
12.836,10.117
6.3534,5.4974
5.4069,0.55657
6.8825,3.9115
11.708,5.3854
5.7737,2.4406
7.8247,6.7318
7.0931,1.0463
5.0702,5.1337
5.8014,1.844
11.7,8.0043
5.5416,1.0179
7.5402,6.7504
5.3077,1.8396
7.4239,4.2885
7.6031,4.9981
6.3328,1.4233
6.3589,-1.4211
6.2742,2.4756
5.6397,4.6042
9.3102,3.9624
9.4536,5.4141
8.8254,5.1694
5.1793,-0.74279
21.279,17.929
14.908,12.054
18.959,17.054
7.2182,4.8852
8.2951,5.7442
10.236,7.7754
5.4994,1.0173
20.341,20.992
10.136,6.6799
7.3345,4.0259
6.0062,1.2784
7.2259,3.3411
5.0269,-2.6807
6.5479,0.29678
7.5386,3.8845
5.0365,5.7014
10.274,6.7526
5.1077,2.0576
5.7292,0.47953
5.1884,0.20421
6.3557,0.67861
9.7687,7.5435
6.5159,5.3436
8.5172,4.2415
9.1802,6.7981
6.002,0.92695
5.5204,0.152
5.0594,2.8214
5.7077,1.8451
7.6366,4.2959
5.8707,7.2029
5.3054,1.9869
8.2934,0.14454
13.394,9.0551
5.4369,0.61705

Posted in Azure, Machine Learning, Tensorflow | Tagged: , | Leave a Comment »

Linear Regression With TensorFlow – Part1

Posted by vivekcek on September 18, 2017

Hi Guys in this blog post i am trying to explain, how we can implement simple linear regression with tensorflow. Simple linear regression means regression with single input and single output.

In future posts i will explain about.

1. Linear regression with multiple features.
2. Polynomial regression.
3. Regularized/Normalized linear regression.
4. Linear regression with external data.

Today anyway i want keep the problem simple, so i am using some inline data for analysis.

I hope you have some good knowledge about Machine Learning. If not please take a training from course-era. Course-era has a good training in Machine Learning by Andrew Ng.

Do we really need tensorflow to do linear regression? We can implement it in Octave, MATLAB, Python, Scikit-learn etc…

Understanding the Math’s and statics behind linear regression is more important than the tool we are going to use.

So how we will approach this problem?

First we need to ensure the data available with us is linearly dependent. For that we need to plot it. You can use the below code to plot your data.

import matplotlib.pyplot as plt
import numpy 

train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()

From the image it is clear that the data is linearly dependent and we can use simple linear regression with it.

Next we are going to define our hypothesis, Cost function and Optimizer. Hope the reader is aware of what you mean by cost, how to minimize the cost etc..

For linear regression the hypothesis we are going to use is the equation of a straight line.

hypothesis (prediction)=WX+b(Where W is the slope and b is the y intercept and X is our input).
In tensorflow we say them as Weight (W) and bias (b).

Next what is cost, cost is actually the difference from the actual to predicted. We need to minimize this cost to find a best W and b.

The cost function for linear regression is given below.

To minimize the cost we are going to use gradient descent algorithm.

The full code is given below.

from __future__ import print_function

import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
rng = numpy.random


learning_rate = 0.01
training_epochs = 1000
display_step = 50


train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0]


X = tf.placeholder("float")
Y = tf.placeholder("float")


W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")


hypothesis = tf.add(tf.multiply(X, W), b)


cost = tf.reduce_mean(tf.square(hypothesis - Y))

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)


init = tf.global_variables_initializer()


with tf.Session() as sess:


    sess.run(init)
    for epoch in range(training_epochs):
        sess.run(optimizer, feed_dict={X: train_X, Y: train_Y})


        if (epoch+1) % display_step == 0:
            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
                "W=", sess.run(W), "b=", sess.run(b))

    print("Optimization Finished!")
    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
    print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')

    # Graphic display
    plt.plot(train_X, train_Y, 'ro', label='Original data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()

    # Testing example, as requested (Issue #2)
    test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
    test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])

    print("Testing... (Mean square loss Comparison)")
    testing_cost = sess.run(
        cost,
        feed_dict={X: test_X, Y: test_Y})  # same function as cost above
    print("Testing cost=", testing_cost)
    print("Absolute mean square loss difference:", abs(
        training_cost - testing_cost))

    plt.plot(test_X, test_Y, 'bo', label='Testing data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()

Posted in Machine Learning, Tensorflow | Leave a Comment »

C# Versions 8 to 6 Fetaures

Posted by vivekcek on September 14, 2017

In this post you can find the new features available in C# versions 8, 7.1, 7, 6.

Posted in c#.net | Tagged: , , , , | Leave a Comment »