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v1.15.1 SMap Do not use BLAS on Windows
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Lines changed: 47 additions & 12 deletions

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‎doc/cppEDM.pdf‎

1.43 KB
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‎src/API.h‎

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Original file line numberDiff line numberDiff line change
@@ -359,6 +359,7 @@ DataFrame< double > PredictNonlinear( std::string pathIn = "./data/",
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bool verbose = true,
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std::vector<bool> validLib =
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std::vector<bool>(),
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bool ignoreNan = true,
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unsigned nThreads = 4 );
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DataFrame< double > PredictNonlinear( DataFrame< double > & dataFrameIn,
@@ -378,5 +379,6 @@ DataFrame< double > PredictNonlinear( DataFrame< double > & dataFrameIn,
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bool verbose = true,
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std::vector<bool> validLib =
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std::vector<bool>(),
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bool ignoreNan = true,
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unsigned nThreads = 4 );
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#endif

‎src/Eval.cc‎

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@@ -79,7 +79,8 @@ void SMapThread( EDM_Eval::WorkQueue &workQ,
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std::string target,
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bool embedded,
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bool verbose,
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std::vector<bool> validLib );
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std::vector<bool> validLib,
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bool ignoreNan );
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//----------------------------------------------------------------
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// EmbedDimension() : Evaluate Simplex rho vs. dimension E
@@ -497,6 +498,7 @@ DataFrame< double > PredictNonlinear( std::string pathIn,
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bool embedded,
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bool verbose,
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std::vector<bool> validLib,
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bool ignoreNan,
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unsigned nThreads ) {
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// Create DataFrame (constructor loads data)
@@ -518,6 +520,7 @@ DataFrame< double > PredictNonlinear( std::string pathIn,
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embedded,
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verbose,
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validLib,
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ignoreNan,
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nThreads );
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return Theta_rho;
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}
@@ -542,6 +545,7 @@ DataFrame< double > PredictNonlinear( DataFrame< double > & data,
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bool embedded,
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bool verbose,
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std::vector<bool> validLib,
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bool ignoreNan,
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unsigned nThreads ) {
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std::vector<double> ThetaValues( { 0.01, 0.1, 0.3, 0.5, 0.75, 1,
@@ -600,7 +604,8 @@ DataFrame< double > PredictNonlinear( DataFrame< double > & data,
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target,
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embedded,
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verbose,
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validLib ) );
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validLib,
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ignoreNan ) );
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}
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// join threads
@@ -647,7 +652,8 @@ void SMapThread( EDM_Eval::WorkQueue &workQ,
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std::string target,
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bool embedded,
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bool verbose,
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std::vector<bool> validLib )
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std::vector<bool> validLib,
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bool ignoreNan )
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{
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std::size_t i =
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std::atomic_fetch_add( &EDM_Eval::smap_count_i, std::size_t(1) );
@@ -679,7 +685,8 @@ void SMapThread( EDM_Eval::WorkQueue &workQ,
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embedded,
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false, // const_predict
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verbose,
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validLib );
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validLib,
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ignoreNan );
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DataFrame< double > predictions = S.predictions;
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DataFrame< double > coefficients = S.coefficients;

‎src/Parameter.cc‎

Lines changed: 4 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -102,7 +102,7 @@ Parameters::Parameters(
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validated ( false ),
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// Instantiate Version
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version( 1, 15, 0, "2023-10-21" )
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version( 1, 15, 1, "2023-10-27" )
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{
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// Constructor code
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if ( method != Method::None ) {
@@ -184,14 +184,15 @@ void Parameters::Validate() {
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if ( randomLib ) {
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if ( subSamples < 1 ) {
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std::string errMsg( "Parameters::Validate(): "
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"CCM samples must be > 0.\n" );
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"CCM sample must be > 0.\n" );
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throw std::runtime_error( errMsg );
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}
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}
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// CCM librarySizes
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if ( not libSizes_str.size() ) {
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std::string errMsg( "Parameters::Validate(): CCM libSize empty.\n" );
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std::string errMsg( "Parameters::Validate(): "
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"CCM libSizes empty.\n" );
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throw std::runtime_error( errMsg );
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}
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‎src/SMap.cc‎

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@@ -490,6 +490,11 @@ void SMapClass::WriteOutput () {
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}
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}
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//>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
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// Do not use LAPACK on Windog: use scikit-learn LinearRegression
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//>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
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#if !defined _WIN32 || defined USING_R
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//----------------------------------------------------------------
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// Singular Value Decomposition : wrapper for Lapack_SVD()
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//----------------------------------------------------------------
@@ -504,11 +509,11 @@ SVDValues SVD( DataFrame < double > A,
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505510
double *b = &( B[0] );
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SVDValues SVD_ = Lapack_SVD( A.NRows(), // number of rows
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A.NColumns(), // number of columns
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a, // A
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b, // b
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1.E-9 ); // rcond
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SVDValues SVD_ = Lapack_SVD( A.NRows(), // number of rows
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A.NColumns(), // number of columns
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a, // A
515+
b, // b
516+
1.E-9 ); // rcond
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#ifdef DEBUG_ALL
514519
std::cout << "SVD------------------------\n";
@@ -657,3 +662,18 @@ SVDValues Lapack_SVD( int m, // rows in matrix
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658663
return SVD_;
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}
665+
#else
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//-----------------------------------------------------------------
667+
// Singular Value Decomposition : SVD() dummy function: Replaced by
668+
// scikit-learn LinearRegression on Windog. LAPACK is not feasible.
669+
//-----------------------------------------------------------------
670+
SVDValues SVD( DataFrame < double > A,
671+
std::valarray< double > B ) {
672+
if ( A.NRows() == B.size() ) {} // Avoid compiler unused warn
673+
SVDValues SVD_;
674+
return SVD_;
675+
}
676+
#endif
677+
//<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
678+
// Do not use LAPACK on Windog: use scikit-learn LinearRegression
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//<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<

‎src/SMap.h‎

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Original file line numberDiff line numberDiff line change
@@ -11,11 +11,16 @@ using Solver = SVDValues (*) ( DataFrame < double >,
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// Prototype declaration of general functions
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SVDValues SVD( DataFrame < double > A, std::valarray< double > B );
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14+
//>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
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// Do not use LAPACK on Windog: use scikit-learn LinearRegression
16+
//>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
17+
#if !defined _WIN32 || defined USING_R
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SVDValues Lapack_SVD( int m, // number of rows in matrix
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int n, // number of columns in matrix
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double *a, // pointer to top-left corner
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double *b,
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double rcond );
23+
#endif
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//----------------------------------------------------------------
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// SMap class inherits from EDM class and defines

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