lisitsyn

103 Blocks are allocated:
Memory block at 0x880d9b8 of size 1472 bytes (allocated in modshogun_wrap.cxx line 191453)
SGObject 'SimpleFeatures' at 0x880d3f0 of size 120 bytes with 2 ref's
Object at 0x878f488 of size 28 bytes
Memory block at 0x880d470 of size 4 bytes (allocated in ../shogun/base/DynArray.h line 49)
Object at 0x8789d38 of size 28 bytes
Memory block at 0x880d678 of size 4 bytes (allocated in ../shogun/base/DynArray.h line 49)
Object at 0x878a030 of size 32 bytes
Object at 0x880d898 of size 32 bytes
Object at 0x880d8f0 of size 32 bytes

lisitsyn

// Compiling with  g++ Shogun.cpp -lshogun -o shoguntest


#include <shogun/features/Labels.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/classifier/svm/LibSVM.h>
#include <shogun/base/init.h>
#include <shogun/lib/common.h>
#include <shogun/io/SGIO.h>

lisitsyn

import numpy
def calcroc(out, lab):
    """
      Computes the ROC curve.
      Expects labels to be +/-1 and real-valued predictions
    """
    idx = numpy.argsort(out)
    tp=numpy.concatenate( ([1], 1-numpy.cumsum(lab[idx]>0)/float(numpy.sum(lab > 0))) )
    fp=numpy.concatenate( ([1], 1-numpy.cumsum(lab[idx]<0)/float(numpy.sum(lab < 0))) )
    score = 0.5*numpy.sum((fp[:-1]-fp[1:])*(tp[:-1]+tp[1:]))

lisitsyn

call pathogen#infect()
syntax on
filetype plugin indent on
let g:load_doxygen_syntax=1

set ruler
set smartindent
set ai
set showmatch
set rulerformat=%(%l,%c\ %p%%%)

lisitsyn

from modshogun import *
from numpy import *
import random 

tree_adj = genfromtxt('tree.tsv', skiprows=1)[:,1:]
features = genfromtxt('features.tsv',skiprows=1)[:,1:]
labels = genfromtxt('labels.tsv',dtype=float)[:,1:].flatten()
n_dims, n_vecs = features.shape

norm = NormOne()

lisitsyn

#include <eigen3/Eigen/Dense>
#include <cblas.h>
#include <stdlib.h>
#include <stdio.h>
#include <time.h>

using namespace Eigen;

static double loop_dot(double* a, double* b, int d)
{

lisitsyn

from pylab import *
from numpy import *
from scipy.optimize import fmin_l_bfgs_b,check_grad
import time

random.seed(7)
N=25
X=hstack([random.randn(2,N)-1.5,random.randn(2,N)+1.5])
mean = X.mean(1)
for i in xrange(N*2):

lisitsyn

import csv
import numpy
import time

class operation():
	def __init__(self, msg):
		self.msg = msg
	def __enter__(self):
		print 'Starting %s' % self.msg
		self.start = time.time()

lisitsyn

import csv
import numpy
import time
import modshogun

vars = {}

class operation():
	def __init__(self, msg, *args):
		self.msg = msg

lisitsyn

import csv
import numpy
import time,datetime
import modshogun
import cPickle

vars = {}

# operational context
class operation(object):