dormantroot

using Newtonsoft.Json;
using RabbitMQ.Client;
using RabbitMQ.Client.Events;
using System;

namespace RabbitMQSample
{
    class Program
    {
        static void Main(string[] args)

dormantroot

def check_test_data(test_X, test_y, all_theta):
    """ USAGE: This method applies the optimized model to the test data set, with the theta values found after optimizing the cost function. 
    
    PARAMETERS:
    test_X: the test data set
    test_y: the test set's true results
    all_theta: coefficients/theta values
    
    RETURN:
    """    

dormantroot

def sigmoid(z):
    """ USAGE:
    Compute the sigmoid of each value of z (z can be a matrix, vector or scalar).            
   
    PARAMETERS:
    z - Matrix, vector or scalar       
    
    RETURN:
    The sigmoid value
    """    

dormantroot

    """NOTE: The following a simple two class classifier example"""
    """From the Iris dataset, find setosa and versicolour Iris types."""
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target
       
    setosa_y = np.where(y == 0)
    versicolour_y = np.where(y ==1)   
    
    X_stacked = np.vstack((X[setosa_y], X[versicolour_y]))

dormantroot

34.62365962451697,78.0246928153624,0
30.28671076822607,43.89499752400101,0
35.84740876993872,72.90219802708364,0
60.18259938620976,86.30855209546826,1
79.0327360507101,75.3443764369103,1
45.08327747668339,56.3163717815305,0
61.10666453684766,96.51142588489624,1
75.02474556738889,46.55401354116538,1
76.09878670226257,87.42056971926803,1
84.43281996120035,43.53339331072109,1

dormantroot

import sys
import pylab as pl
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
import matplotlib.pyplot as plt
import matplotlib as matplot
import numpy as np
from scipy.optimize import fmin_bfgs
from sklearn import linear_model