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@@ -271,7 +271,7 @@ void FPCRandomForests::train(FeaturePool & fp, Examples & examples)
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}
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}
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- fprintf(stderr, "FPCRandomForests: minimum number of examples: %f (classno: %d)\n", minExamples, minExamplesClassNo);
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+ fprintf(stderr, "FPCRandomForests: minimum number of examples: %d (classno: %d)\n", (int)minExamples, minExamplesClassNo);
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int featuresCount = (int)(fp.size() * features_per_tree);
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fprintf(stderr, "FPCRandomForests: number of features %d\n", (int)fp.size());
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@@ -288,7 +288,7 @@ void FPCRandomForests::train(FeaturePool & fp, Examples & examples)
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#pragma omp parallel for
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for (int k = 0 ; k < number_of_trees ; k++)
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{
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- fprintf(stderr, "[ -- building tree %d/%d -- ]\n", k + 1, number_of_trees);
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+ fprintf(stderr, "FPCRandomForests: [ -- building tree %d/%d -- ]\n", k + 1, number_of_trees);
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FeaturePool fp_subset;
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Examples examples_subset;
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@@ -357,7 +357,8 @@ void FPCRandomForests::train(FeaturePool & fp, Examples & examples)
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/******* training of an individual tree ****/
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DecisionTree *tree = new DecisionTree(conf, maxClassNo);
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- builder->build(*tree, fp_subset, examples_subset, maxClassNo);
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+ #pragma omp critical
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+ builder->build(*tree, fp_subset, examples_subset, maxClassNo);
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/******* prune tree using a simple minimum entropy criterion *****/
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if (minimum_entropy != 0.0)
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