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add duplicate option check and drop unused functions

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This commit is contained in:
Keon Kim
2016-06-07 13:28:37 +09:00
parent e931116326
commit b86e427e4c
6 changed files with 50 additions and 77 deletions
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.gitignore
+3
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@@ -6,3 +6,6 @@ build*
*.bak
src/mlpack/core/util/gitversion.hpp
src/mlpack/core/util/arma_config.hpp
.idea
src/mlpack/core/util/cli.cpp
+2 -35
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@@ -6,16 +6,12 @@
*/
#include <list>
#include <boost/program_options.hpp>
#include <boost/any.hpp>
#include <boost/scoped_ptr.hpp>
#include <iostream>
#include <string>
#include "cli.hpp"
#include "log.hpp"
#include "option.hpp"
using namespace mlpack;
using namespace mlpack::util;
@@ -108,7 +104,8 @@ void CLI::Add(const std::string& identifier,
po::options_description& desc = CLI::GetSingleton().desc;
// Must make use of boost option name syntax.
std::string progOptId = alias.length() ? identifier + "," + alias : identifier;
std::string progOptId =
alias.length() ? identifier + "," + alias : identifier;
// Deal with a required alias.
AddAlias(alias, identifier);
@@ -453,36 +450,6 @@ void CLI::RemoveDuplicateFlags(po::basic_parsed_options<char>& bpo)
}
}
/**
* Parses a stream for arguments
*
* @param stream The stream to be parsed.
*/
void CLI::ParseStream(std::istream& stream)
{
po::variables_map& vmap = GetSingleton().vmap;
po::options_description& desc = GetSingleton().desc;
// Parse the stream; place options & values into vmap.
try
{
po::store(po::parse_config_file(stream, desc), vmap);
}
catch (std::exception& ex)
{
Log::Fatal << ex.what() << std::endl;
}
// Flush the buffer; make sure changes are propagated to vmap.
po::notify(vmap);
UpdateGmap();
DefaultMessages();
RequiredOptions();
Timer::Start("total_time");
}
/* Prints out the current hierarchy. */
void CLI::Print()
{
src/mlpack/core/util/cli.hpp
+1 -17
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@@ -635,13 +635,6 @@ class CLI
*/
static void RemoveDuplicateFlags(po::basic_parsed_options<char>& bpo);
/**
* Parses a stream for arguments.
*
* @param stream The stream to be parsed.
*/
static void ParseStream(std::istream& stream);
/**
* Print out the current hierarchy.
*/
@@ -673,7 +666,7 @@ class CLI
//! Values of the options given by user.
po::variables_map vmap;
//! Pathnames of required options.
//! Identifier names of required options.
std::list<std::string> requiredOptions;
//! Map of global values.
@@ -728,15 +721,6 @@ class CLI
*/
static void RequiredOptions();
/**
* Cleans up input pathnames, rendering strings such as /foo/bar
* and foo/bar/ equivalent inputs.
*
* @param str Input string.
* @return Sanitized string.
*/
static std::string SanitizeString(const std::string& str);
/**
* Parses the values given on the command line, overriding any default values.
*/
src/mlpack/core/util/cli_impl.hpp
+22 -3
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@@ -9,10 +9,20 @@
// In case it has not already been included.
#include "cli.hpp"
#include "prefixedoutstream.hpp"
// Include option.hpp here because it requires CLI but is also templated.
#include "option.hpp"
// Color code escape sequences.
#ifndef _WIN32
#define BASH_RED "\033[0;31m"
#define BASH_CLEAR "\033[0m"
#else
#define BASH_RED ""
#define BASH_CLEAR ""
#endif
namespace mlpack {
/**
@@ -33,10 +43,21 @@ void CLI::Add(const std::string& identifier,
const std::string& alias,
bool required)
{
util::PrefixedOutStream outstr(std::cerr,
BASH_RED "[FATAL] " BASH_CLEAR, false, true /* fatal */);
gmap_t& gmap = GetSingleton().globalValues;
amap_t& amap = GetSingleton().aliasValues;
if (gmap.count(identifier))
outstr << "Parameter --" << identifier << "(-" << alias << ") "
<< "is defined multiple times with same identifiers." << std::endl;
if (amap.count(alias))
outstr << "Parameter --" << identifier << "(-" << alias << ") "
<< "is defined multiple times with same alias." << std::endl;
po::options_description& desc = CLI::GetSingleton().desc;
// Must make use of boost syntax here.
std::string progOptId = alias.length() ? identifier + "," + alias : identifier;
std::string progOptId =
alias.length() ? identifier + "," + alias : identifier;
// Add the alias, if necessary
AddAlias(alias, identifier);
@@ -45,8 +66,6 @@ void CLI::Add(const std::string& identifier,
desc.add_options()(progOptId.c_str(), po::value<T>(), description.c_str());
// Make sure the appropriate metadata is inserted into gmap.
gmap_t& gmap = GetSingleton().globalValues;
ParamData data;
T tmp = T();
src/mlpack/methods/ann/ffn.hpp
+12 -12
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@@ -22,7 +22,7 @@ namespace ann /** Artificial Neural Network. */ {
* Implementation of a standard feed forward network.
*
* @tparam LayerTypes Contains all layer modules used to construct the network.
* @tparam OutputLayerType The outputlayer type used to evaluate the network.
* @tparam OutputLayerType The output layer type used to evaluate the network.
* @tparam InitializationRuleType Rule used to initialize the weight matrix.
* @tparam PerformanceFunction Performance strategy used to calculate the error.
*/
@@ -48,14 +48,14 @@ class FFN
* be used.
*
* @param network Network modules used to construct the network.
* @param outputLayer Outputlayer used to evaluate the network.
* @param outputLayer Output layer used to evaluate the network.
* @param predictors Input training variables.
* @param responses Outputs resulting from input training variables.
* @param optimizer Instantiated optimizer used to train the model.
* @param initializeRule Optional instantiated InitializationRule object
* for initializing the network paramter.
* for initializing the network parameter.
* @param performanceFunction Optional instantiated PerformanceFunction
* object used to claculate the error.
* object used to calculate the error.
*/
template<typename LayerType,
typename OutputType,
@@ -74,13 +74,13 @@ class FFN
* initialize rule and performance function should be used.
*
* @param network Network modules used to construct the network.
* @param outputLayer Outputlayer used to evaluate the network.
* @param outputLayer Output layer used to evaluate the network.
* @param predictors Input training variables.
* @param responses Outputs resulting from input training variables.
* @param initializeRule Optional instantiated InitializationRule object
* for initializing the network paramter.
* for initializing the network parameter.
* @param performanceFunction Optional instantiated PerformanceFunction
* object used to claculate the error.
* object used to calculate the error.
*/
template<typename LayerType, typename OutputType>
FFN(LayerType &&network,
@@ -96,11 +96,11 @@ class FFN
* training.
*
* @param network Network modules used to construct the network.
* @param outputLayer Outputlayer used to evaluate the network.
* @param outputLayer Output layer used to evaluate the network.
* @param initializeRule Optional instantiated InitializationRule object
* for initializing the network paramter.
* for initializing the network parameter.
* @param performanceFunction Optional instantiated PerformanceFunction
* object used to claculate the error.
* object used to calculate the error.
*/
template<typename LayerType, typename OutputType>
FFN(LayerType &&network,
@@ -408,10 +408,10 @@ private:
//! Instantiated feedforward network.
LayerTypes network;
//! The outputlayer used to evaluate the network
//! The output layer used to evaluate the network
OutputLayerType outputLayer;
//! Performance strategy used to claculate the error.
//! Performance strategy used to calculate the error.
PerformanceFunction performanceFunc;
//! The current evaluation mode (training or testing).
src/mlpack/methods/ann/rnn.hpp
+10 -10
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@@ -24,7 +24,7 @@ namespace ann /** Artificial Neural Network. */ {
* Implementation of a standard recurrent neural network.
*
* @tparam LayerTypes Contains all layer modules used to construct the network.
* @tparam OutputLayerType The outputlayer type used to evaluate the network.
* @tparam OutputLayerType The output layer type used to evaluate the network.
* @tparam InitializationRuleType Rule used to initialize the weight matrix.
* @tparam PerformanceFunction Performance strategy used to calculate the error.
*/
@@ -50,14 +50,14 @@ class RNN
* be used.
*
* @param network Network modules used to construct the network.
* @param outputLayer Outputlayer used to evaluate the network.
* @param outputLayer Output layer used to evaluate the network.
* @param predictors Input training variables.
* @param responses Outputs resulting from input training variables.
* @param optimizer Instantiated optimizer used to train the model.
* @param initializeRule Optional instantiated InitializationRule object
* for initializing the network paramter.
* for initializing the network parameter.
* @param performanceFunction Optional instantiated PerformanceFunction
* object used to claculate the error.
* object used to calculate the error.
*/
template<typename LayerType,
typename OutputType,
@@ -76,13 +76,13 @@ class RNN
* initialize rule and performance function should be used.
*
* @param network Network modules used to construct the network.
* @param outputLayer Outputlayer used to evaluate the network.
* @param outputLayer Output layer used to evaluate the network.
* @param predictors Input training variables.
* @param responses Outputs resulting from input training variables.
* @param initializeRule Optional instantiated InitializationRule object
* for initializing the network paramter.
* for initializing the network parameter.
* @param performanceFunction Optional instantiated PerformanceFunction
* object used to claculate the error.
* object used to calculate the error.
*/
template<typename LayerType, typename OutputType>
RNN(LayerType &&network,
@@ -98,11 +98,11 @@ class RNN
* training.
*
* @param network Network modules used to construct the network.
* @param outputLayer Outputlayer used to evaluate the network.
* @param outputLayer Output layer used to evaluate the network.
* @param initializeRule Optional instantiated InitializationRule object
* for initializing the network paramter.
* for initializing the network parameter.
* @param performanceFunction Optional instantiated PerformanceFunction
* object used to claculate the error.
* object used to calculate the error.
*/
template<typename LayerType, typename OutputType>
RNN(LayerType &&network,