diff --git a/BayesianKDEy/full_experiments.py b/BayesianKDEy/full_experiments.py
new file mode 100644
index 0000000..ec5acaa
--- /dev/null
+++ b/BayesianKDEy/full_experiments.py
@@ -0,0 +1,75 @@
+import warnings
+
+from sklearn.linear_model import LogisticRegression
+import quapy as qp
+from BayesianKDEy._bayeisan_kdey import BayesianKDEy
+from method.aggregative import AggregativeQuantifier
+from quapy.model_selection import GridSearchQ
+from quapy.data import Dataset
+# from BayesianKDEy.plot_simplex import plot_prev_points, plot_prev_points_matplot
+from quapy.method.confidence import ConfidenceIntervals, BayesianCC, PQ
+from quapy.functional import strprev
+from quapy.method.aggregative import KDEyML
+from quapy.protocol import UPP
+import quapy.functional as F
+import numpy as np
+from tqdm import tqdm
+from scipy.stats import dirichlet
+
+
+def new_classifier():
+    lr_hyper = {
+        'classifier__C': np.logspace(-3,3,7),
+        'classifier__class_weight': ['balanced', None]
+    }
+    lr = LogisticRegression()
+    return lr, lr_hyper
+
+def methods():
+    cls, cls_hyper = new_classifier()
+    # yield 'BayesianACC', BayesianCC(cls, mcmc_seed=0), cls_hyper
+    # yield 'BayesianHDy', PQ(cls, stan_seed=0), {**cls_hyper, 'n_bins': [3,4,5,8,16,32]}
+    yield 'BayesianKDEy', BayesianKDEy(cls, mcmc_seed=0), {**cls_hyper, 'bandwidth': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}
+
+
+def experiment(dataset: Dataset, method: AggregativeQuantifier, method_name: str, grid: dict):
+    with qp.util.temp_seed(0):
+        # model selection
+        train, test = dataset.train_test
+        train, val  = train.split_stratified(train_prop=0.6, random_state=0)
+        mod_sel = GridSearchQ(
+            model=method,
+            param_grid=grid,
+            protocol=qp.protocol.UPP(val, repeats=250, random_state=0),
+            refit=True,
+            n_jobs=-1,
+            verbose=True
+        ).fit(*train.Xy)
+        optim_quantifier = mod_sel.best_model()
+        optim_hyper = mod_sel.best_params_
+        print(f'model_selection for {method_name} ended: chosen hyper-params {optim_hyper}')
+
+        # test
+        report = qp.evaluation.evaluation_report(
+            optim_quantifier,
+            protocol=UPP(test, repeats=500, random_state=0),
+            verbose=True
+        )
+
+        return report
+
+
+
+if __name__ == '__main__':
+    qp.environ["SAMPLE_SIZE"] = 500
+
+    datasets = qp.datasets.UCI_BINARY_DATASETS
+    for dataset in datasets:
+        data = qp.datasets.fetch_UCIBinaryDataset(dataset)
+        for method_name, method, hyper_params in methods():
+            report = experiment(data, method, method_name, hyper_params)
+            print(f'{method_name=} got {report.mean(numeric_only=True)}')
+
+
+
+