arunreddy

#!/bin/ bash

# VAR1=$1
# VAR2=$2
# VAR3=$3

python example.py ${VAR1}  ${VAR1}  ${VAR1} 
------------------------------------------------------------------------------------
Inside the python file.

arunreddy

# ---------------------------------------------------------
# library imports.

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.dates import DateFormatter, DayLocator, HourLocator

# *********************************************************

arunreddy

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.dates import DateFormatter
from datetime import timedelta, date


def datetime(x):
  return np.array(x, dtype = np.datetime64)

arunreddy

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.dates import DateFormatter, MinuteLocator, MonthLocator


def datetime(x):
  return np.array(x, dtype = np.datetime64)

arunreddy

// SGD with variadic templates for policy type class.
template<typename DecomposableFunctionType, typename ... PolicyType>
class SGD{
}

// Use alias to declare most frequently used combinations.
using StandardSGD = SGD<FunctionType, EmptyUpdate, NoDecay>;
using SGD2 = SGD<FunctionType, MomentumUpdate, NoDecay>;
using SGD3 = SGD<FunctionType, NesterovUpdate, ExponentialDecay>;

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#include <iostream>

using namespace std;

int main()
{
   cout << "Hello World" << endl; 
   count << "Does is sync" << endl;
   
   return 0;

arunreddy

#include <iostream>

using namespace std;

int main()
{
   cout << "Hello World" << endl; 
   
   return 0;
}

arunreddy

// logistic_regression_test.cpp Lines[504-512]
// Very simple fake dataset.
arma::mat data("1 2 3;"
               "1 2 3");
arma::Row<size_t> responses("1 1 0");

// Create a logistic regression object using a custom SGD object with a much
// smaller tolerance.
LogisticRegressionFunction<> lrf(data, responses, 0.001);

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StandardSGD <LogisticRegressionFunction<>> sgdOpt(lrf);
sgdOpt.MaxIterations() = maxIterations;
sgdOpt.Tolerance() = tolerance;
sgdOpt.StepSize() = stepSize;
Log::Info << "Training model with SGD optimizer." << endl;

// This will train the model.
model.Train(sgdOpt);

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// Without policy class..
Optimize(arma::mat& iterate){
  
  arma::mat v = arma::zeros<arma::mat>(iterate.n_rows, iterate.n_cols);
  
  for(;;){
    
    v = momentum*v - stepSize * gradient;
    iterate += v;
  }