Removed unnecessary methods from LARS

shogun-toolbox · May 1, 2012 · ca57115 · ca57115
1 parent 40c2582
commit ca57115
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diff --git a/src/shogun/regression/LARS.cpp b/src/shogun/regression/LARS.cpp
@@ -327,20 +327,6 @@ bool CLARS::train_machine(CFeatures* data)
 	return true;
 }
 
-bool CLARS::load(FILE* srcfile)
-{
-	SG_SET_LOCALE_C;
-	SG_RESET_LOCALE;
-	return false;
-}
-
-bool CLARS::save(FILE* dstfile)
-{
-	SG_SET_LOCALE_C;
-	SG_RESET_LOCALE;
-	return false;
-}
-
 void CLARS::cholesky_insert(const SGMatrix<float64_t> &X, SGMatrix<float64_t> &R, int32_t i_max_corr)
 {
 	// diag_k = X[:,i_max_corr]' * X[:,i_max_corr]

diff --git a/src/shogun/regression/LARS.h b/src/shogun/regression/LARS.h
@@ -142,59 +142,6 @@ class CLARS: public CLinearMachine
 		return m_beta_idx.size();
 	}
 
-	/** get w
-	 *
-	 * @param num_var number of non-zero coefficients
-	 * @param dst_w store w in this argument
-	 * @param dst_dims dimension of w
-	 *
-	 * **Note** the returned memory references to some internal structures. The
-	 * pointer will become invalid if train is called *again*. So make a copy
-	 * if you want to call train multiple times.
-	 *
-	 * @see switch_w
-	 */
-	void get_w(int32_t num_var, float64_t*& dst_w, int32_t& dst_dims)
-	{
-		if (w_dim <= 0)
-			SG_ERROR("cannot get estimator before training");
-		if (size_t(num_var) >= m_beta_idx.size() || num_var < 0)
-			SG_ERROR("cannot get an estimator of %d non-zero coefficients", num_var);
-		dst_dims=w_dim;
-		dst_w=&m_beta_path[m_beta_idx[num_var]][0];
-	}
-
-	/** get w
-	 *
-	 * @param num_var number of non-zero coefficients
-	 *
-	 * @return the estimator with num_var non-zero coefficients. **Note** the
-	 * returned memory references to some internal structures. The pointer will
-	 * become invalid if train is called *again*. So make a copy if you want to
-	 * call train multiple times.
-	 */
-	SGVector<float64_t> get_w(int32_t num_var)
-	{
-		SGVector<float64_t> vec;
-		get_w(num_var, vec.vector, vec.vlen);
-		vec.do_free = false;
-		return vec;
-	}
-
-	/** load regression from file
-	 *
-	 * @param srcfile file to load from
-	 * @return if loading was successful
-	 */
-	virtual bool load(FILE* srcfile);
-
-	/** save regression to file
-	 *
-	 * @param dstfile file to save to
-	 * @return if saving was successful
-	 */
-	virtual bool save(FILE* dstfile);
-
 	/** get classifier type
 	 *
 	 * @return classifier type LinearRidgeRegression