madaan

package edu.washington.multir.experiment;

import java.io.IOException;
import java.util.List;

import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.util.CoreMap;
import edu.stanford.nlp.util.Pair;

madaan

data=evstr(read_csv("~/cs215/session_22_07/data.txt"))
X=data(:, 1:2);
X=[ones(size(X, 1), 1) X];
y=data(:, 3);
W = inv(X' * X) * X' * y;
predictions = X * W;
plot(predictions, 'r');
plot(y, 'g');
legend(["Predictions"; "True Value"]);
title("Linear Regression")

madaan

//0. Read the dataset!

//1. loading dataset
data=read_csv("~/cs215/session_22_07/train.csv");
header = data(1, :); //save the header for description
data=evstr(data(2:$, :));
ageIn = 4;
genderIn = 3;
classIn = 2;
fareIn = 7;

madaan

#sg
command='curl -O -C - http://www.mpi-inf.mpg.de/yago-naga/aida/download/entity-repository/AIDA_entity_repository_2010-08-17v5-1.sql.bz2'

restarts=0
CLICK_TIME=5
until `$command`;do
    echo "Download stopped!"
    #keep taking snapshots
    if [ `echo "$restart % $CLICK_TIME" | bc` -eq 0 ];then
        cp AIDA_entity_repository_2010-08-17v5-1.sql.bz2 aida_backup

madaan

// shf.go
package main

import (
	"github.com/madaan/shuffler"
)

func main() {
	shuffler.Shuffle_sentence("All work and no play makes jack a dull boy")
}

madaan

def cvalidate():
    from sklearn import cross_validation

    trainset = np.genfromtxt(open('train.csv','r'), delimiter=',')[1:]
    X = np.array([x[1:8] for x in trainset])
    y = np.array([x[8] for x in trainset])
    #print X,y
    import math
    for i, x in enumerate(X):

madaan

def main():
    targetset = np.genfromtxt(open('trainLabels.csv','r'), dtype='f16')
    target = [x for x in targetset]

    trainset = np.genfromtxt(open('train.csv','r'), delimiter=',', dtype='f16')
    train = np.array([x for x in trainset])


    testset = np.genfromtxt(open('test.csv','r'), delimiter = ',',  dtype='f16')
    train, testset = decomposition_pca(train, testset)

madaan

def cvalidate():
    targetset = np.genfromtxt(open('trainLabels.csv','r'), dtype='f16')
    y = [x for x in targetset]

    trainset = np.genfromtxt(open('train.csv','r'), delimiter=',', dtype='f16')
    X = np.array([x for x in trainset])
    
    X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size = 0.3, random_state = 0)

    X_train, X_test = decomposition_pca(X_train, X_test)