andfoy

sudo npm install -g node-red-contrib-mongodb2 &&  sudo npm install -g node-red-dashboard        &&  sudo npm install -g node-red-node-arduino     &&  sudo npm install -g node-red-node-mongodb     &&  sudo npm install -g node-red-node-random

andfoy

DECLARE
sql_stmt VARCHAR(1000);
csr SYS_REFCURSOR;
table_name VARCHAR(31);
n_cols DOUBLE PRECISION;
n_fks DOUBLE PRECISION;
n_rows DOUBLE PRECISION;
BEGIN
FOR x IN (SELECT INST FROM 
    (SELECT TABLE_NAME, 'SELECT b.TABLE_NAME NOMBRETABLA, NVL(NUMFK, 0), NUMCOLS, NUMFILAS FROM

andfoy

// isis1304-111-proyecto2.cpp: define el punto de entrada de la aplicación de consola.
//
// DESARROLLADO POR:
// Daniel Ordoñez, 201327156
// Edgar Margffoy, 201412566
// Camila Garcia,  201326493

#define _CRT_SECURE_NO_WARNINGS
#include <stdlib.h> 
#include <stdio.h>

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/**
 * JavaCC template file created by SF JavaCC plugin 1.5.17+ wizard for JavaCC 1.5.0+
*/



options
{
  static = false;
  DEBUG_PARSER = true;

andfoy

#include <Wire.h>
#include <string.h>
//#include <AltSoftSerial.h>


/**
   Macro definitions
**/
#define Task_t 10          // Task Time in milli seconds

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def applyGCN(save_image=False,doTrain=False,doTest=False):
    data = np.loadtxt("../inputData/train.csv", dtype=np.float32, delimiter=',', skiprows=1)
    test_data = np.loadtxt("../inputData/test.csv", dtype=np.float32, delimiter=',', skiprows=1)
 
    trainingFolder = "../inputData/converted_training/GCN/"
    testingFolder = "../inputData/converted_testing/GCN/"
 
    if(doTrain):
        aux_data = data.copy()
 

andfoy

import os
import numpy as np
from __future__ import division

def flatten_matrix(mat):
    mat = mat.flatten(1)
    mat = mat.reshape(len(mat), 1, order='F')
    return mat

def logistic(x):

andfoy

#This program is distributed WITHOUT ANY WARRANTY; without even the implied 
#warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 
#
#
#This file contains a Python version of Carl Rasmussen's Matlab-function 
#minimize.m
#
#minimize.m is copyright (C) 1999 - 2006, Carl Edward Rasmussen.
#Python adaptation by Roland Memisevic 2008.
#

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function [cost,grad,features] = sparseLinearNNCost(theta, visibleSize, hiddenSize, ...
                                                   outputSize, lambda, sparsityParam, beta, data, labels)


W1 = reshape(theta(1:hiddenSize*visibleSize), hiddenSize, visibleSize);
W2 = reshape(theta(hiddenSize*visibleSize+1:(hiddenSize*visibleSize) + (hiddenSize*outputSize)), outputSize, hiddenSize);
b1 = theta((hiddenSize*visibleSize) + (hiddenSize*outputSize) + 1:(hiddenSize*visibleSize) + (hiddenSize*outputSize)  + hiddenSize);
b2 = theta((hiddenSize*visibleSize) + (hiddenSize*outputSize) + 1 + hiddenSize:end);

andfoy

// isis1304-111-proyecto2.cpp: define el punto de entrada de la aplicación de consola.
//
// DESARROLLADO POR:
// Nombre, carnet
// Nombre, carnet
// Nombre, carnet
#define _CRT_SECURE_NO_WARNINGS
#include "stdlib.h"
#include "stdio.h"
#include "string.h"