1. Ruben Martinez-Cantin
  2. BayesOpt

Commits

Ruben Martinez-Cantin  committed 6782023

Solving problem with random number initialization

  • Participants
  • Parent commits b65843d
  • Branches default

Comments (0)

Files changed (10)

File doxygen/reference.dox

View file
 optimization, we can choose among different strategies for the initial
 design (1-Latin Hypercube Sampling (LHS), 2-Sobol sequences (if available,
 see \ref mininst), Other-Uniform Sampling) [Default 1, LHS].
+\li \b use_random_seed: 0-Fixed seed, 1-Time based (variable)
+seed. For debugging purposes, it might be useful to freeze the random
+seed. [Default 1, variable seed].
 
 
 \subsection logpar Logging parameters

File examples/bo_cont.cpp

View file
   par.kernel.name = "kSum(kSEISO,kConst)";
   par.mean.name = "mConst";
   par.sc_type = SC_ML;
-  par.n_iterations = 200;       // Number of iterations
+  par.n_iterations = 200;    // Number of iterations
+  par.init_method = 1;
   par.n_init_samples = 50;
   par.n_iter_relearn = 20;
   par.verbose_level = 2;

File include/parameters.h

View file
     /** Sampling method for initial set 1-LHS, 2-Sobol (if available),
      *  other value-uniformly distributed */
     size_t init_method;          
+    size_t use_random_seed;      /**< 0-Fixed seed, 1-Random (time) seed.*/    
 
     size_t verbose_level;        /**< 1-Error,2-Warning,3-Info. 4-6 log file*/
     char* log_filename;          /**< Log file path (if applicable) */

File matlab/bayesoptextras.h

View file
   struct_size(params,"n_inner_iterations", &parameters.n_inner_iterations);
   struct_size(params, "n_init_samples", &parameters.n_init_samples);
   struct_size(params, "n_iter_relearn", &parameters.n_iter_relearn);
+
   struct_size(params, "init_method", &parameters.init_method);
+  struct_size(params, "use_random_seed", &parameters.use_random_seed);
   
   struct_size(params, "verbose_level", &parameters.verbose_level);
   struct_string(params, "log_filename", parameters.log_filename);

File python/bayesopt.cpp

View file
-/* Generated by Cython 0.19 on Wed Mar 19 00:36:54 2014 */
+/* Generated by Cython 0.19 on Thu Mar 20 20:22:08 2014 */
 
 #define PY_SSIZE_T_CLEAN
 #ifndef CYTHON_USE_PYLONG_INTERNALS
 static char __pyx_k__n_init_samples[] = "n_init_samples";
 static char __pyx_k__n_iter_relearn[] = "n_iter_relearn";
 static char __pyx_k____pyx_getbuffer[] = "__pyx_getbuffer";
+static char __pyx_k__use_random_seed[] = "use_random_seed";
 static char __pyx_k__sGaussianProcess[] = "sGaussianProcess";
 static char __pyx_k__ascontiguousarray[] = "ascontiguousarray";
 static char __pyx_k__initialize_params[] = "initialize_params";
 static PyObject *__pyx_n_s__sigma_s;
 static PyObject *__pyx_n_s__surr_name;
 static PyObject *__pyx_n_s__ub;
+static PyObject *__pyx_n_s__use_random_seed;
 static PyObject *__pyx_n_s__valid_x;
 static PyObject *__pyx_n_s__verbose_level;
 static PyObject *__pyx_n_s__x;
 static PyObject *__pyx_k_codeobj_18;
 static PyObject *__pyx_k_codeobj_20;
 
-/* "bayesopt.pyx":96
+/* "bayesopt.pyx":99
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   __Pyx_RefNannySetupContext("dict2structparams", 0);
 
-  /* "bayesopt.pyx":98
+  /* "bayesopt.pyx":101
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  *     params = initialize_parameters_to_default()             # <<<<<<<<<<<<<<
  */
   __pyx_v_params = initialize_parameters_to_default();
 
-  /* "bayesopt.pyx":100
+  /* "bayesopt.pyx":103
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-  /* "bayesopt.pyx":101
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  *     params.n_inner_iterations = dparams.get('n_inner_iterations',             # <<<<<<<<<<<<<<
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+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 104; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "bayesopt.pyx":102
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  *     params.n_iterations = dparams.get('n_iterations',params.n_iterations)
  *     params.n_inner_iterations = dparams.get('n_inner_iterations',
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  *     params.n_init_samples = dparams.get('n_init_samples',params.n_init_samples)
  *     params.n_iter_relearn = dparams.get('n_iter_relearn',params.n_iter_relearn)
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  *     params.n_inner_iterations = dparams.get('n_inner_iterations',             # <<<<<<<<<<<<<<
  */
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-  /* "bayesopt.pyx":103
+  /* "bayesopt.pyx":106
  *     params.n_inner_iterations = dparams.get('n_inner_iterations',
  *                                             params.n_inner_iterations)
  *     params.n_init_samples = dparams.get('n_init_samples',params.n_init_samples)             # <<<<<<<<<<<<<<
  *     params.n_iter_relearn = dparams.get('n_iter_relearn',params.n_iter_relearn)
- *     params.init_method = dparams.get('init_method',params.init_method)
+ * 
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+  __pyx_t_1 = PyLong_FromUnsignedLong(__pyx_v_params.n_init_samples); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 106; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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  *     params.n_iter_relearn = dparams.get('n_iter_relearn',params.n_iter_relearn)             # <<<<<<<<<<<<<<
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- *     params.init_method = dparams.get('init_method',params.init_method)             # <<<<<<<<<<<<<<
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