Cleanup and optimize transformers - #4348
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vinx13
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Jun 28, 2018
vinx13
commented
- Use linalg in NormOne
- Optimize PruneVarSubMean
vinx13
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This needs a rebase |
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Btw whenever you make something linalg, pls ensure there is some test coverage of the changes lines, so that we at least know he results didn’t change in the integration test examples |
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| EXPECT_EQ(v.vlen, num_features); | ||
| for (auto j : range(v.vlen)) | ||
| { | ||
| EXPECT_DOUBLE_EQ(v[j], data[num_features * i + j] / norm[i]); |
| { | ||
| SGVector<float64_t> v = feats->get_feature_vector(i); | ||
| auto result = transformer->apply_to_feature_vector(v); | ||
| EXPECT_EQ(result.vlen, num_features); |
| CPruneVarSubMean::apply_to_matrix(SGMatrix<float64_t> matrix) | ||
| { | ||
| ASSERT(m_initialized) | ||
| REQUIRE(m_initialized, "Transformer has not been fitted.\n"); |
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offtopic: I would love to move all those general checks to base classes so that they dont get repeated in every specialization....
any thoughts on this
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shouldn't we use a different exception type other than ShogunException
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| CPruneVarSubMean::apply_to_matrix(SGMatrix<float64_t> matrix) | ||
| { | ||
| ASSERT(m_initialized) | ||
| REQUIRE(m_initialized, "Transformer has not been fitted.\n"); |
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shouldn't we use a different exception type other than ShogunException
| SGVector<float64_t> CPruneVarSubMean::apply_to_feature_vector(SGVector<float64_t> vector) | ||
| { | ||
| float64_t* ret=NULL; | ||
| REQUIRE(m_initialized, "Transformer has not been fitted.\n"); |
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shouldn't we use a different exception type other than ShogunException
| MS_NOT_AVAILABLE); | ||
| } | ||
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| void CTransformer::check_fitted() const |
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i would rather go with the java like: is_fitted or is_fit depending which english we'd like to go with :) but let's stick to british (non north american english as in case of the exception type naming)
| CTransformer::CTransformer() : CSGObject() | ||
| { | ||
| SG_ADD( | ||
| &m_fitted, "fitted", "Whether the transformer has been fitted.", |
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let's register this param as is_fitted
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