refactor: Update register names

Ангелина · Ангелина · commit 7e47e6369baa · 2025-05-31T15:45:14.000+03:00
diff --git a/tests/algorithms/nm_algorithms/semiparametric_sigma_estimation/test_sigma_estimation_eigenvalue_based.py b/tests/algorithms/nm_algorithms/semiparametric_sigma_estimation/test_sigma_estimation_eigenvalue_based.py
@@ -29,7 +29,7 @@ def test_sigma_estimation_eigenvalue_based_expon_single(
         )
 
         estimator = NMSemiParametricEstimator(
-            "sigma_estimation_eigenvalue_based", {"k": k, "l": l, "eps": eps, "search_area": search_area}
+            "sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}
         )
         est = estimator.estimate(sample)
         print(est.value)
@@ -55,7 +55,7 @@ def test_sigma_estimation_eigenvalue_based_expon_1(
         )
 
         estimator = NMSemiParametricEstimator(
-            "sigma_estimation_eigenvalue_based", {"k": k, "l": l, "eps": eps, "search_area": search_area}
+            "sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}
         )
         est = estimator.estimate(sample)
         print(est.value)
@@ -81,7 +81,7 @@ def test_sigma_estimation_eigenvalue_based_expon_2(
         )
 
         estimator = NMSemiParametricEstimator(
-            "sigma_estimation_eigenvalue_based", {"k": k, "l": l, "eps": eps, "search_area": search_area}
+            "sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}
         )
         est = estimator.estimate(sample)
         print(est.value)
@@ -107,7 +107,7 @@ def test_sigma_estimation_eigenvalue_based_expon_3(
         )
 
         estimator = NMSemiParametricEstimator(
-            "sigma_estimation_eigenvalue_based", {"k": k, "l": l, "eps": eps, "search_area": search_area}
+            "sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}
         )
         est = estimator.estimate(sample)
         print(est.value)
@@ -132,7 +132,7 @@ def test_sigma_estimation_eigenvalue_based_uniform(
         )
 
         estimator = NMSemiParametricEstimator(
-            "sigma_estimation_eigenvalue_based", {"k": k, "l": l, "eps": eps, "search_area": search_area}
+            "sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}
         )
         est = estimator.estimate(sample)
         print(est.value)
diff --git a/tests/algorithms/nm_algorithms/semiparametric_sigma_estimation/test_sigma_estimation_empirical.py b/tests/algorithms/nm_algorithms/semiparametric_sigma_estimation/test_sigma_estimation_empirical.py
@@ -20,7 +20,7 @@ def test_sigma_estimation_empirical_expon(self, real_sigma: float, sample_len: i
         answer_list = []
         for alpha in [round(x, 5) for x in [i * 0.0001 for i in range(1, 10000)]]:
             t = math.sqrt(alpha * math.log(sample_len)) / (2 * search_area)
-            estimator = NMSemiParametricEstimator("sigma_estimation_empirical", {"t": t})
+            estimator = NMSemiParametricEstimator("sigma_estimation_laplace", {"t": t})
             est = estimator.estimate(sample)
             left = (est.value**2 - real_sigma**2) ** 0.5
             right = (
@@ -39,7 +39,7 @@ def test_sigma_estimation_empirical_uniform(self, real_sigma: float, sample_len:
         M = 10
         for alpha in [round(x, 4) for x in [i * 0.0001 for i in range(1, 10000)]]:
             t = math.sqrt(alpha * math.log(sample_len)) / (2 * search_area)
-            estimator = NMSemiParametricEstimator("sigma_estimation_empirical", {"t": t})
+            estimator = NMSemiParametricEstimator("sigma_estimation_laplace", {"t": t})
             est = estimator.estimate(sample)
             left = abs(est.value**2 - real_sigma**2) ** 0.5
             right = 4 * M * search_area / math.sqrt(alpha * math.log(sample_len))
diff --git a/tests/algorithms/nmv_algorithms/semiparametric_g_estimation/test_g_estimation_given_mu.py b/tests/algorithms/nmv_algorithms/semiparametric_g_estimation/test_g_estimation_given_mu.py
@@ -19,7 +19,7 @@ def test_g_estimation_expon(self, x_data) -> None:
         mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=given_mu, distribution=expon)
         sample = NMVGenerator().canonical_generate(mixture, n)
         estimator = NMVSemiParametricEstimator(
-            "g_estimation_given_mu", {"x_data": x_data, "u_value": 7.6, "v_value": 0.9}
+            "density_estim_inv_mellin_quad_int", {"x_data": x_data, "u_value": 7.6, "v_value": 0.9}
         )
         est = estimator.estimate(sample)
         error = 0.0
diff --git a/tests/algorithms/nmv_algorithms/semiparametric_g_estimation/test_post_widder.py b/tests/algorithms/nmv_algorithms/semiparametric_g_estimation/test_post_widder.py
@@ -21,7 +21,7 @@ def test_post_widder_expon(self, mu, sigma, degree, sample_size) -> None:
         x_data = np.linspace(0.5, 10.0, 30)
 
         estimator = NMVSemiParametricEstimator(
-            "g_estimation_post_widder", {"x_data": x_data, "mu": mu, "sigma": sigma, "n": degree}
+            "density_estim_post_widder", {"x_data": x_data, "mu": mu, "sigma": sigma, "n": degree}
         )
         est = estimator.estimate(sample)
         est_data = est.list_value
@@ -44,7 +44,7 @@ def test_post_widder_gamma(self, mu, sigma, degree, sample_size, a) -> None:
         x_data = np.linspace(0.5, 10.0, 30)
 
         estimator = NMVSemiParametricEstimator(
-            "g_estimation_post_widder", {"x_data": x_data, "mu": mu, "sigma": sigma, "n": degree}
+            "density_estim_post_widder", {"x_data": x_data, "mu": mu, "sigma": sigma, "n": degree}
         )
         est = estimator.estimate(sample)
         est_data = est.list_value
diff --git a/tests/algorithms/nv_algorithms/test_g_estimation.py b/tests/algorithms/nv_algorithms/test_g_estimation.py
@@ -18,7 +18,7 @@ def test_g_estimation_expon(self, x_data) -> None:
         mixture = NormalVarianceMixtures("canonical", alpha=0, distribution=expon)
         sample = NVGenerator().canonical_generate(mixture, n)
         estimator = NVSemiParametricEstimator(
-            "g_estimation_given_mu", {"x_data": x_data, "u_value": 7.6, "v_value": 0.9}
+            "density_estim_inv_mellin_quad_int", {"x_data": x_data, "u_value": 7.6, "v_value": 0.9}
         )
         est = estimator.estimate(sample)
         error = 0.0
diff --git a/tests/algorithms/nv_algorithms/test_post_widder_nv.py b/tests/algorithms/nv_algorithms/test_post_widder_nv.py
@@ -21,7 +21,7 @@ def test_post_widder_expon(self, sigma, degree, sample_size) -> None:
         x_data = np.linspace(0.5, 10.0, 30)
 
         estimator = NVSemiParametricEstimator(
-            "g_estimation_post_widder", {"x_data": x_data, "sigma": sigma, "n": degree}
+            "density_estim_post_widder", {"x_data": x_data, "sigma": sigma, "n": degree}
         )
         est = estimator.estimate(sample)
         est_data = est.list_value
@@ -44,7 +44,7 @@ def test_post_widder_gamma(self, sigma, degree, sample_size, a) -> None:
         x_data = np.linspace(0.5, 10.0, 30)
 
         estimator = NVSemiParametricEstimator(
-            "g_estimation_post_widder", {"x_data": x_data, "sigma": sigma, "n": degree}
+            "density_estim_post_widder", {"x_data": x_data, "sigma": sigma, "n": degree}
         )
         est = estimator.estimate(sample)
         est_data = est.list_value

Original file line number	Diff line number	Diff line change
`@@ -29,7 +29,7 @@ def test_sigma_estimation_eigenvalue_based_expon_single(`
`29`	`29`	`)`
`30`	`30`
`31`	`31`	`estimator = NMSemiParametricEstimator(`
`32`		`- "sigma_estimation_eigenvalue_based", {"k": k, "l": l, "eps": eps, "search_area": search_area}`
	`32`	`+ "sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}`
`33`	`33`	`)`
`34`	`34`	`est = estimator.estimate(sample)`
`35`	`35`	`print(est.value)`
`@@ -55,7 +55,7 @@ def test_sigma_estimation_eigenvalue_based_expon_1(`
`55`	`55`	`)`
`56`	`56`
`57`	`57`	`estimator = NMSemiParametricEstimator(`
`58`		`- "sigma_estimation_eigenvalue_based", {"k": k, "l": l, "eps": eps, "search_area": search_area}`
	`58`	`+ "sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}`
`59`	`59`	`)`
`60`	`60`	`est = estimator.estimate(sample)`
`61`	`61`	`print(est.value)`
`@@ -81,7 +81,7 @@ def test_sigma_estimation_eigenvalue_based_expon_2(`
`81`	`81`	`)`
`82`	`82`
`83`	`83`	`estimator = NMSemiParametricEstimator(`
`84`		`- "sigma_estimation_eigenvalue_based", {"k": k, "l": l, "eps": eps, "search_area": search_area}`
	`84`	`+ "sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}`
`85`	`85`	`)`
`86`	`86`	`est = estimator.estimate(sample)`
`87`	`87`	`print(est.value)`
`@@ -107,7 +107,7 @@ def test_sigma_estimation_eigenvalue_based_expon_3(`
`107`	`107`	`)`
`108`	`108`
`109`	`109`	`estimator = NMSemiParametricEstimator(`
`110`		`- "sigma_estimation_eigenvalue_based", {"k": k, "l": l, "eps": eps, "search_area": search_area}`
	`110`	`+ "sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}`
`111`	`111`	`)`
`112`	`112`	`est = estimator.estimate(sample)`
`113`	`113`	`print(est.value)`
`@@ -132,7 +132,7 @@ def test_sigma_estimation_eigenvalue_based_uniform(`
`132`	`132`	`)`
`133`	`133`
`134`	`134`	`estimator = NMSemiParametricEstimator(`
`135`		`- "sigma_estimation_eigenvalue_based", {"k": k, "l": l, "eps": eps, "search_area": search_area}`
	`135`	`+ "sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}`
`136`	`136`	`)`
`137`	`137`	`est = estimator.estimate(sample)`
`138`	`138`	`print(est.value)`
Original file line number	Diff line number	Diff line change
`@@ -19,7 +19,7 @@ def test_g_estimation_expon(self, x_data) -> None:`
`19`	`19`	`mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=given_mu, distribution=expon)`
`20`	`20`	`sample = NMVGenerator().canonical_generate(mixture, n)`
`21`	`21`	`estimator = NMVSemiParametricEstimator(`
`22`		`- "g_estimation_given_mu", {"x_data": x_data, "u_value": 7.6, "v_value": 0.9}`
	`22`	`+ "density_estim_inv_mellin_quad_int", {"x_data": x_data, "u_value": 7.6, "v_value": 0.9}`
`23`	`23`	`)`
`24`	`24`	`est = estimator.estimate(sample)`
`25`	`25`	`error = 0.0`
Original file line number	Diff line number	Diff line change
`@@ -18,7 +18,7 @@ def test_g_estimation_expon(self, x_data) -> None:`
`18`	`18`	`mixture = NormalVarianceMixtures("canonical", alpha=0, distribution=expon)`
`19`	`19`	`sample = NVGenerator().canonical_generate(mixture, n)`
`20`	`20`	`estimator = NVSemiParametricEstimator(`
`21`		`- "g_estimation_given_mu", {"x_data": x_data, "u_value": 7.6, "v_value": 0.9}`
	`21`	`+ "density_estim_inv_mellin_quad_int", {"x_data": x_data, "u_value": 7.6, "v_value": 0.9}`
`22`	`22`	`)`
`23`	`23`	`est = estimator.estimate(sample)`
`24`	`24`	`error = 0.0`