Merge pull request #31 from mu373/fix-return-data-type

Fix return data type

Author: mu373

README.md

@@ -36,11 +36,11 @@ data = TailData(name='CAIDA_KONECT').data
 estimator = HillEstimator()
 estimator.fit(data)
 
-# Get the estimated parameters
-result = estimator.get_parameters()
+# Get the estimated results
+result = estimator.get_result()
 
 # Get the power law exponent
-gamma = result.gamma
+gamma = result.gamma_
 
 # Print full results
 print(result)
@@ -59,11 +59,11 @@ degree = list(dict(G.degree()).values()) # Degree sequence
 estimator = HillEstimator()
 estimator.fit(degree)
 
-# Get the estimated parameters
-result = estimator.get_parameters()
+# Get the estimated results
+result = estimator.get_result()
 
 # Get the power law exponent
-gamma = result.gamma
+gamma = result.gamma_
 
 # Print full results
 print(result)
@@ -87,10 +87,10 @@ The package provides several estimators for tail estimation. For details on para
    - Smoothed version of the Hill estimator (no bootstrap)
 
 ## Results
-The full result can be obtained by `estimator.get_parameters()`, which returns a dictionary. This includes:
-- `gamma`: Power law exponent (γ = 1 + 1/ξ)
-- `xi_star`: Tail index (ξ)
-- `k_star`: Optimal order statistic
+The full result can be obtained by `estimator.get_result()`, which is a TailEstimatorResult object. This includes attributes such as:
+- `gamma_`: Power law exponent (γ = 1 + 1/ξ)
+- `xi_star_`: Tail index (ξ)
+- `k_star_`: Optimal order statistic
 - Bootstrap results (when applicable):
   - First and second bootstrap AMSE values
   - Optimal bandwidths or minimum AMSE fractions

docs/estimators/hill.rst

@@ -22,5 +22,5 @@ Examples
     hill = HillEstimator()
     hill.fit(data)
     
-    # Get estimated parameters
-    params = hill.get_parameters()
+    # Get estimated values
+    res = hill.get_result()

docs/estimators/kernel.rst

@@ -27,5 +27,5 @@ Examples
     )
     kernel.fit(data)
     
-    # Get estimated parameters
-    params = kernel.get_parameters()
+    # Get estimated values
+    res = kernel.get_result()

docs/estimators/moments.rst

@@ -23,5 +23,5 @@ Examples
     moments = MomentsEstimator()
     moments.fit(data)
     
-    # Get estimated parameters
-    params = moments.get_parameters()
+    # Get estimated values
+    res = moments.get_result()

docs/estimators/pickands.rst

@@ -22,5 +22,5 @@ Examples
     pickands = PickandsEstimator()
     pickands.fit(data)
     
-    # Get estimated parameters
-    params = pickands.get_parameters()
+    # Get estimated values
+    res = pickands.get_result()

docs/estimators/smooth_hill.rst

@@ -22,6 +22,6 @@ Examples
     smooth_hill = SmoothHillEstimator()
     smooth_hill.fit(data)
     
-    # Get estimated parameters
-    params = smooth_hill.get_parameters()
+    # Get estimated values
+    res = smooth_hill.get_result()
 

docs/result.rst

@@ -19,11 +19,11 @@ Examples
    hill = HillEstimator()
    hill.fit(data)
    
-   # Get estimated parameters
-   result = hill.get_parameters() # This returns TailEstimatorResult class.
+   # Get estimated values
+   result = hill.get_result() # This returns TailEstimatorResult class.
    print(result)
 
    # Access individual parameters
-   gamma = result.gamma  # Power-law exponent estimate
-   xi = result.xi  # Tail index estimate
+   gamma = result.gamma_  # Power-law exponent estimate
+   xi = result.xi_  # Tail index estimate
  

docs/usage.rst

@@ -32,9 +32,9 @@ Using Built-in Datasets
     estimator = HillEstimator()
     estimator.fit(data)
 
-    # Get the estimated parameters
-    result = estimator.get_parameters()
-    gamma = result.gamma
+    # Get estimated values
+    result = estimator.get_result()
+    gamma = result.gamma_
 
     # Print full results
     print(result)
@@ -55,9 +55,9 @@ Using degree sequence from networkx graphs
     estimator = HillEstimator()
     estimator.fit(degree)
 
-    # Get the estimated parameters
-    result = estimator.get_parameters()
-    gamma = result.gamma
+    # Get estimated values
+    result = estimator.get_result()
+    gamma = result.gamma_
 
     # Print full results
     print(result)
@@ -84,11 +84,11 @@ The package provides several estimators for tail estimation. For details on each
 Results
 -------
 
-The full result can be obtained by ``result = estimator.get_parameters()``. You can either print the result, or access individual attributes (e.g., `result.gamma`). The output will include:
+The full result can be obtained by ``result = estimator.get_result()``. You can either print the result, or access individual attributes (e.g., `result.gamma_`). The output will include values such as:
 
-- ``gamma``: Power law exponent (γ = 1 + 1/ξ)
-- ``xi_star``: Tail index (ξ)
-- ``k_star``: Optimal order statistic
+- ``gamma_``: Power law exponent (γ = 1 + 1/ξ)
+- ``xi_star_``: Tail index (ξ)
+- ``k_star_``: Optimal order statistic
 - Bootstrap results (when applicable):
   - First and second bootstrap AMSE values
   - Optimal bandwidths or minimum AMSE fractions

examples/example.ipynb

@@ -15,11 +15,14 @@
 estimator.fit(data)
 print(estimator)
 
-# Get the estimated parameters
-result = estimator.get_parameters()
+# Get the parameters of the estimator
+params = estimator.get_params()
+
+# Get estimated values
+result = estimator.get_result()
 
 # Get the power law exponent
-gamma = result.gamma
+gamma = result.gamma_
 
 # Print full results
 print(result)