--- a
+++ b/python/aux1/evaluation_cm.py
@@ -0,0 +1,232 @@
+#!/usr/bin/env python
+
+"""
+evaluation_cnn.py
+    
+Description:
+    Auxiliar file to compute performance measures given the confussion matrix
+
+VARPA, University of Coruna
+Mondejar Guerra, Victor M.
+31 Jan 2018
+"""
+
+from sklearn import metrics
+import numpy as np
+
+class performance_measures:
+    def __init__(self, n):
+        self.n_classes          = n
+        self.confusion_matrix   = np.empty([])
+        self.Recall             = np.empty(n)
+        self.Precision          = np.empty(n)
+        self.Specificity        = np.empty(n)
+        self.Acc                = np.empty(n)
+        self.F_measure          = np.empty(n)
+
+        self.gmean_se          = 0.0
+        self.gmean_p      #!/usr/bin/env python
+
+"""
+train_SVM.py
+    
+VARPA, University of Coruna
+Mondejar Guerra, Victor M.
+26 Oct 2017
+"""
+
+from sklearn import metrics
+import numpy as np
+
+class performance_measures:
+    def __init__(self, n):
+        self.n_classes          = n
+        self.confusion_matrix   = np.empty([])
+        self.Recall             = np.empty(n)
+        self.Precision          = np.empty(n)
+        self.Specificity        = np.empty(n)
+        self.Acc                = np.empty(n)
+        self.F_measure          = np.empty(n)
+
+        self.gmean_se          = 0.0
+        self.gmean_p           = 0.0
+
+        self.Overall_Acc        = 0.0
+        self.kappa              = 0.0
+        self.Ij                 = 0.0
+        self.Ijk                = 0.0
+
+        self.Overall_Acc        = 0.0
+        self.kappa              = 0.0
+        self.Ij                 = 0.0
+        self.Ijk                = 0.0
+
+
+# Compute Cohen' kappa from a confussion matrix
+# Kappa value:
+#    < 0.20  Poor
+# 0.21-0.40  Fair
+# 0.41-0.60  Moderate
+# 0.61-0.80  Good
+# 0.81-1.00  Very good
+def compute_cohen_kappa(confusion_matrix):
+    prob_expectedA = np.empty(len(confusion_matrix))
+    prob_expectedB = np.empty(len(confusion_matrix))
+    prob_observed = 0
+    
+    for n in range(0, len(confusion_matrix)):
+        prob_expectedA[n] = sum(confusion_matrix[n,:]) / sum(sum(confusion_matrix))
+        prob_expectedB[n] = sum(confusion_matrix[:,n]) / sum(sum(confusion_matrix))
+    
+        prob_observed = prob_observed + confusion_matrix[n][n]
+
+    prob_expected = np.dot(prob_expectedA, prob_expectedB)
+    prob_observed = prob_observed / sum(sum(confusion_matrix))  
+
+    kappa = (prob_observed - prob_expected) / (1 - prob_expected)
+
+    return kappa, prob_observed, prob_expected
+
+
+
+# Export to filename.txt file the performance measure score
+def write_AAMI_results(performance_measures, filename):
+
+    f = open(filename, "w") 
+
+    f.write("Ijk: " + str(format(performance_measures.Ijk, '.4f')) + "\n")
+    f.write("Ij: " + str(format(performance_measures.Ij, '.4f'))+ "\n")
+    f.write("Cohen's Kappa: " + str(format(performance_measures.kappa, '.4f'))+ "\n\n")
+
+    # Conf matrix
+    f.write("Confusion Matrix:"+ "\n\n")
+    f.write("\n".join(str(elem) for elem in performance_measures.confusion_matrix.astype(int))+ "\n\n")
+
+    f.write("Overall ACC: " + str(format(performance_measures.Overall_Acc, '.4f'))+ "\n\n")
+
+    f.write("mean Acc: " + str(format(np.average(performance_measures.Acc[:]), '.4f'))+ "\n")
+    f.write("mean Recall: " + str(format(np.average(performance_measures.Recall[:]), '.4f'))+ "\n")
+    f.write("mean Precision: " + str(format(np.average(performance_measures.Precision[:]), '.4f'))+ "\n")
+  
+
+    f.write("N:"+ "\n\n")
+    f.write("Sens: " + str(format(performance_measures.Recall[0], '.4f'))+ "\n")
+    f.write("Prec: " + str(format(performance_measures.Precision[0], '.4f'))+ "\n")
+    f.write("Acc: " + str(format(performance_measures.Acc[0], '.4f'))+ "\n")
+
+    f.write("SVEB:"+ "\n\n")
+    f.write("Sens: " + str(format(performance_measures.Recall[1], '.4f'))+ "\n")
+    f.write("Prec: " + str(format(performance_measures.Precision[1], '.4f'))+ "\n")
+    f.write("Acc: " + str(format(performance_measures.Acc[1], '.4f'))+ "\n")
+
+    f.write("VEB:"+ "\n\n")
+    f.write("Sens: " + str(format(performance_measures.Recall[2], '.4f'))+ "\n")
+    f.write("Prec: " + str(format(performance_measures.Precision[2], '.4f'))+ "\n")
+    f.write("Acc: " + str(format(performance_measures.Acc[2], '.4f'))+ "\n")
+
+    f.write("F:"+ "\n\n")
+    f.write("Sens: " + str(format(performance_measures.Recall[3], '.4f'))+ "\n")
+    f.write("Prec: " + str(format(performance_measures.Precision[3], '.4f'))+ "\n")
+    f.write("Acc: " + str(format(performance_measures.Acc[3], '.4f'))+ "\n")
+
+
+    f.close()
+
+
+
+
+
+def compute_AAMI_performance_measures(conf_mat):
+    n_classes = 4 #5
+    pf_ms = performance_measures(n_classes)
+
+    pf_ms.confusion_matrix = conf_mat
+
+    # Overall Acc
+    pf_ms.Overall_Acc = 0.0
+
+    # AAMI: Sens, Spec, Acc 
+    # N: 0, S: 1, V: 2, F: 3                        # (Q: 4) not used
+    for i in range(0, n_classes):
+        TP = conf_mat[i,i]
+        FP = sum(conf_mat[:,i]) - conf_mat[i,i]
+        TN = sum(sum(conf_mat)) - sum(conf_mat[i,:]) - sum(conf_mat[:,i]) + conf_mat[i,i]
+        FN = sum(conf_mat[i,:]) - conf_mat[i,i]
+
+        if i == 2: # V 
+            # Exceptions for AAMI recomendations:
+            # 1 do not reward or penalize a classifier for the classification of (F) as (V)
+            FP = FP - conf_mat[i][3]
+        
+        pf_ms.Recall[i]       = TP / (TP + FN)
+        pf_ms.Precision[i]    = TP / (TP + FP)
+        pf_ms.Specificity[i]  = TN / (TN + FP); # 1-FPR
+        pf_ms.Acc[i]          = (TP + TN) / (TP + TN + FP + FN)
+
+        if TP == 0:
+            pf_ms.F_measure[i] = 0.0
+        else:
+            pf_ms.F_measure[i] = 2 * (pf_ms.Precision[i] * pf_ms.Recall[i] )/ (pf_ms.Precision[i] + pf_ms.Recall[i])
+
+    # Compute Cohen'    # TODO If conf_mat no llega a clases 4 por gt_labels o predictions...
+    # hacer algo para que no falle el codigo...
+    # NOTE: added labels=[0,1,2,3])...
+
+    # Confussion matrixs Kappa
+    pf_ms.kappa, prob_obsv, prob_expect = compute_cohen_kappa(conf_mat)
+
+    # Compute Index-j   recall_S + recall_V + precision_S + precision_V
+    pf_ms.Ij = pf_ms.Recall[1] + pf_ms.Recall[2] + pf_ms.Precision[1] + pf_ms.Precision[2]
+
+    # Compute Index-jk
+    w1 = 0.5
+    w2 = 0.125
+    pf_ms.Ijk = w1 * pf_ms.kappa + w2 * pf_ms.Ij
+
+    return pf_ms
+
+
+results_path = '/home/mondejar/Dropbox/ECG/code/ecg_classification/python/results/ovo/MLII/'
+
+# Our single SVM
+#conf_mat = np.array([[39446, 2404, 340, 1843], [443, 1374, 186, 47], [28, 162, 3005, 25], [240, 2, 35, 111]])
+
+# Our features individually
+## RR C_0.001_IJK_0.44
+conf_mat = np.array([[33881, 2531, 2385, 5236], [263, 1036, 725, 26], [63, 350, 2584, 223], [43, 2, 4, 339]])
+conf_mat = conf_mat.astype(float)
+perf_measures = compute_AAMI_performance_measures(conf_mat)
+write_AAMI_results( perf_measures, results_path + 'RR_score_Ijk_' + str(format(perf_measures.Ijk, '.2f')) + '_DS2.txt')
+
+
+## HOS
+conf_mat = np.array([[25171, 11907, 1125, 5830], [340, 1474, 39, 197], [212, 486, 2369, 153], [31, 5, 55, 297]])
+conf_mat = conf_mat.astype(float)
+perf_measures = compute_AAMI_performance_measures(conf_mat)
+write_AAMI_results( perf_measures, results_path + 'HOS_score_Ijk_' + str(format(perf_measures.Ijk, '.2f')) + '_DS2.txt')
+
+## W C_0.001 IJK 0.38
+conf_mat = np.array([[37752, 2484, 3722, 75], [1755, 217, 76, 2], [98, 27, 3087, 8], [12, 1, 370, 5]])
+conf_mat = conf_mat.astype(float)
+perf_measures = compute_AAMI_performance_measures(conf_mat)
+write_AAMI_results( perf_measures, results_path + 'W_score_Ijk_' + str(format(perf_measures.Ijk, '.2f')) + '_DS2.txt')
+
+
+## Our Morph C_0.001 IJK 0.35
+conf_mat = np.array([[20601, 11239, 1817, 10376], [327, 1450, 16, 257], [302, 165, 2483, 270], [277, 78, 21, 12]])
+conf_mat = conf_mat.astype(float)
+perf_measures = compute_AAMI_performance_measures(conf_mat)
+write_AAMI_results( perf_measures, results_path + 'Our_morph_score_Ijk_' + str(format(perf_measures.Ijk, '.2f')) + '_DS2.txt')
+
+## LBP C_0.001 IJK 0.17
+conf_mat = np.array([[32771, 1211, 4256, 5795], [1922, 11, 51, 66], [847, 91, 1688, 594], [27, 0, 360, 1]])
+conf_mat = conf_mat.astype(float)
+perf_measures = compute_AAMI_performance_measures(conf_mat)
+write_AAMI_results( perf_measures, results_path + 'LBP_score_Ijk_' + str(format(perf_measures.Ijk, '.2f')) + '_DS2.txt')
+
+# Chazal et al
+#conf_mat = np.array([[38444, 1904, 303, 3509], [173, 1395, 252, 16], [117, 321, 2504, 176], [33, 1, 7, 347]])
+
+#conf_mat = conf_mat.astype(float)
+#perf_measures = compute_AAMI_performance_measures(conf_mat)
+#write_AAMI_results( perf_measures, results_path + 'Chazal_score_Ijk_' + str(format(perf_measures.Ijk, '.2f')) + '_DS2.txt')