mick001

import matplotlib.pyplot as plt
from scipy.integrate import quad
import numpy as np
plt.style.use("ggplot")

Vn = 132000                             # Nominal voltage [V]
E = Vn * np.sqrt(2) / np.sqrt(3)        # Peak phase voltage [V]
l = 80                                  # Line length [km]
L = 0.00048 * l                         # Line inductance [H]
R = 0.0754 * l                          # Line resistance [Ohm]

mick001

import matplotlib.pyplot as plt
import numpy as np
plt.style.use("ggplot")

#-------------------------------------------------------------------------------
# Auxiliary functions

# Complex power
def complex_power(p, q):
    return np.sqrt(p**2 + q**2)

mick001

import numpy as np
import matplotlib.pyplot as plt
plt.style.use("ggplot")

#-------------------------------------------------------------------------------
# Helper functions

# Derive the function f and evaluate it in t
def differentiate(f, t, dt=0.01):
    f_prime = (f(t + dt) - f(t))/dt

mick001

import numpy as np

# R matrix
R = np.matrix([ [-1,-1,-1,-1,0,-1],
		[-1,-1,-1,0,-1,100],
		[-1,-1,-1,0,-1,-1],
		[-1,0,0,-1,0,-1],
		[-1,0,0,-1,-1,100],
		[-1,0,-1,-1,0,100] ])

mick001

import tensorflow as tf
import pandas as pd
from sklearn.cross_validation import train_test_split

FILE_PATH = '~/Desktop/bank-add/bank_equalized.csv'	           # Path to .csv dataset
raw_data = pd.read_csv(FILE_PATH)						                   # Open raw .csv

print("Raw data loaded successfully...\n")
#------------------------------------------------------------------------------
# Variables

mick001

import pandas as pd
import numpy as np
from sklearn import preprocessing

# Load data
data = pd.read_csv('bank-additional-full.csv', sep = ";")
# Variables names
var_names = data.columns.tolist()

# Categorical vars

mick001

################################################################################
# Loading data

rm( list=ls() )

# load libs
require(neuralnet)
require(nnet)

# Load data and set names

mick001

require(neuralnet)  # neuralnet,compute, functions
require(nnet)       # class.ind function

# Clearing workspace
rm( list=ls() )

# Set seed
set.seed(100)

# Loading data

mick001

set.seed(1)

# define some functions

## convert integer to binary
i2b <- function(integer, length=8)
  as.numeric(intToBits(integer))[1:length]

## apply 
int2bin <- function(integer, length=8)

mick001

# Clean workspace
rm(list=ls())

# Load MXNet
require(mxnet)

# Loading data and set up
#-------------------------------------------------------------------------------

# Load train and test datasets