Revert "test modification of example to see if google cloud run it."

This reverts commit 0eae2bf.

examples/ERP/classify_P300_bi.py

@@ -30,7 +30,7 @@
 import seaborn as sns
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
 from moabb import set_log_level
-from moabb.datasets import bi2012, bi2013a
+from moabb.datasets import bi2012
 from moabb.evaluations import WithinSessionEvaluation
 from moabb.paradigms import P300
 from pyriemann_qiskit.classification import \
@@ -62,7 +62,7 @@
 
 paradigm = P300(resample=128)
 
-datasets = [bi2012(), bi2013a()]  # MOABB provides several other P300 datasets
+datasets = [bi2012()]  # MOABB provides several other P300 datasets
 
 # reduce the number of subjects, the Quantum pipeline takes a lot of time
 # if executed on the entire dataset
@@ -74,26 +74,36 @@
 
 pipelines = {}
 
-
-# Here we provide a pipeline for comparison:
-
-# This is a standard pipeline similar to
-# QuantumClassifierWithDefaultRiemannianPipeline, but with LDA classifier
-# instead.
-pipelines["RG+SVM"] = QuantumClassifierWithDefaultRiemannianPipeline(
-    shots=None,  # 'None' forces classic SVM
-    nfilter=2,  # default 2
-    dim_red=PCA(n_components=5),
-    )
-
 # A Riemannian Quantum pipeline provided by pyRiemann-qiskit
 # You can choose between classical SVM and Quantum SVM.
 pipelines["RG+QuantumSVM"] = QuantumClassifierWithDefaultRiemannianPipeline(
-    shots=1024,  # 'None' forces classic SVM
+    shots=None,  # 'None' forces classic SVM
     nfilter=2,  # default 2
+    # default n_components=10, a higher value renders better performance with
+    # the non-qunatum SVM version used in qiskit
+    # On a real Quantum computer (n_components = qubits)
     dim_red=PCA(n_components=5),
+    # params={'q_account_token': '<IBM Quantum TOKEN>'}
     )
 
+# Here we provide a pipeline for comparison:
+
+# This is a standard pipeline similar to
+# QuantumClassifierWithDefaultRiemannianPipeline, but with LDA classifier
+# instead.
+pipelines["RG+LDA"] = make_pipeline(
+    # applies XDawn and calculates the covariance matrix, output it matrices
+    XdawnCovariances(
+        nfilter=2,
+        classes=[labels_dict["Target"]],
+        estimator="lwf",
+        xdawn_estimator="scm"
+    ),
+    TangentSpace(),
+    PCA(n_components=10),
+    LDA(solver="lsqr", shrinkage="auto"),  # you can use other classifiers
+)
+
 print("Total pipelines to evaluate: ", len(pipelines))
 
 evaluation = WithinSessionEvaluation(