geektoni

#include <shogun/base/init.h>
#include <shogun/base/some.h>                                     
#include <shogun/distance/Distance.h>
#include <shogun/features/Features.h>
#include <shogun/io/File.h>
#include <shogun/io/SerializableAsciiFile.h>
#include <shogun/lib/DynamicObjectArray.h>
#include <shogun/lib/SGMatrix.h>
#include <shogun/lib/SGVector.h>
#include <shogun/lib/observers/ParameterObserver.h>

geektoni

# Beta0 e Beta predetti
0.057167247685
[ 0. -0. -0.  0.  0. -0. -0.  0. -0.  0.]

# Beta0 e Beta reali usati dal generatore
[ 0.04515583]
[-0.012569  0.058881  0.138457 -0.021287 -0.021285  0.143565  0.069767
 -0.042679  0.049324 -0.042129]

geektoni

from matplotlib import pyplot as plt
from matplotlib import animation
from shogun import csv_file, features, labels, machine, parameter_observer

f_feats_train = csv_file("classifier_binary_2d_linear_features_train.dat")
f_feats_test = csv_file("classifier_binary_2d_linear_features_test.dat")
f_labels_train = csv_file("classifier_binary_2d_linear_labels_train.dat")
f_labels_test = csv_file("classifier_binary_2d_linear_labels_test.dat")

features_train = features(f_feats_train)

geektoni

/******/
/* How observable parameters can be registered*/
/******/
MyFabulousMachine::init()
{
	// Register standard parameters
	SG_ADD(&m_w, "weights", "Model weigths", ParameterProperties::MODEL);

	// Version A: Register observable parameters. The user could then get a list
  	// (vector) of the parameters he can observe inside this model. Only the parameters

geektoni

#include <shogun/features/DenseFeatures.h>
#include <shogun/lib/SGMatrix.h>    
#include <shogun/regression/LeastAngleRegression.h>
#include <shogun/labels/RegressionLabels.h>
#include <shogun/mathematics/linalg/LinalgNamespace.h>
#include <shogun/preprocessor/PruneVarSubMean.h>
#include <shogun/preprocessor/NormOne.h>
#include <shogun/lib/parameter_observers/ParameterObserverLogger.h>      
#include <shogun/base/init.h>
#include <shogun/lib/common.h>                                                  

geektoni

#!/bin/bash
USER=$1
PASS=$2
usermod --password $(echo $PASS | openssl passwd -1 -stdin) $USER

geektoni

-------------------------------------------------------------------------------------------------
Benchmark                                                          Time           CPU Iterations
-------------------------------------------------------------------------------------------------
# Baseline
DataFixture/perceptron_baseline/8/256                             16 ms         16 ms         43
DataFixture/perceptron_baseline/64/256                            18 ms         18 ms         34
DataFixture/perceptron_baseline/256/256                           47 ms         47 ms         15
DataFixture/perceptron_baseline/8/512                             31 ms         31 ms         22
DataFixture/perceptron_baseline/64/512                            51 ms         51 ms         13
DataFixture/perceptron_baseline/256/512                          108 ms        108 ms          6

geektoni

GSoC 2017 Final Report

Shogun Detox II: Codebase improvements and finalization of the new Tags and Serialization frameworks

Student: Giovanni De Toni
Organization: The Shogun Toolbox
Mentors: Viktor Gal, lambday

Abstract

The Shogun Toolbox is a well-established machine learning project that provides efficient algorithms implementations that can be used in a wide range of applications and with multi-language support (thanks to SWIG magic). Unfortunately, since it was built by many hands for many years, its code has become not easily maintainable or extendable and it does not use many new programming techniques and components that have appeared since the Shogun foundation. The time has come to blow some fresh air (and some new fresh code) into Shogun's depths. This project aims to correct and update the codebase and to complete the integration of

geektoni

#include <shogun/base/init.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/labels/RegressionLabels.h>
#include <shogun/kernel/LinearKernel.h>
#include <shogun/regression/KernelRidgeRegression.h>
#include <shogun/evaluation/CrossValidation.h>
#include <shogun/evaluation/CrossValidationSplitting.h>
#include <shogun/evaluation/MeanSquaredError.h>
#include <shogun/lib/parameter_observers/ParameterObserverCV.h>

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#0  __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:51
#1  0x00007ffff0ea13fa in __GI_abort () at abort.c:89
#2  0x00007ffff0eddbd0 in __libc_message (do_abort=do_abort@entry=2, fmt=fmt@entry=0x7ffff0fd2dd0 "*** Error in `%s': %s: 0x%s ***\n")
    at ../sysdeps/posix/libc_fatal.c:175
#3  0x00007ffff0ee3f96 in malloc_printerr (action=3, str=0x7ffff0fd31d8 "malloc(): memory corruption (fast)", ptr=<optimized out>,
    ar_ptr=<optimized out>) at malloc.c:5049
#4  0x00007ffff0ee6461 in _int_malloc (av=av@entry=0x7ffff1206b00 <main_arena>, bytes=bytes@entry=7) at malloc.c:3424
#5  0x00007ffff0ee7f34 in __GI___libc_malloc (bytes=7) at malloc.c:2928
#6  0x00007ffff5df91da in (anonymous namespace)::sg_malloc (size=7) at /home/geektoni/shogun/src/shogun/lib/memory.cpp:205
#7  0x00007ffff584fe19 in (anonymous namespace)::sg_generic_malloc<unsigned char> (len=7) at /home/geektoni/shogun/src/shogun/lib/memory.h:91