--- a
+++ b/random_forest/rf_test3.py
@@ -0,0 +1,457 @@
+import os
+import sys
+import pywt
+from pywt import wavedec
+from __init__ import ap_entropy, samp_entropy
+import numpy as np
+import matplotlib
+from matplotlib import pyplot as plt
+from mnist_new import mlp, create_training_set
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import roc_curve
+A=[]
+B=[]
+C=[]
+D=[]
+E=[]
+for fl in os.listdir("../../../ALL/A/"):
+                inp = []
+                path = "../../../ALL/A/" + fl
+                txt = open(path,'r')
+                for line in txt:
+                        feature = line.split()[0]
+                        inp.append(feature)
+		a = np.array(inp)
+                A.append(a)
+
+for fl in os.listdir("../../../ALL/B/"):
+                inp = []
+                path = "../../../ALL/B/" + fl
+                txt = open(path,'r')
+                for line in txt:
+                        feature = line.split()[0]
+                        inp.append(feature)
+                a = np.array(inp)
+                B.append(a)
+
+for fl in os.listdir("../../../ALL/C/"):
+                inp = []
+                path = "../../../ALL/C/" + fl
+                txt = open(path,'r')
+                for line in txt:
+                        feature = line.split()[0]
+                        inp.append(feature)
+                a = np.array(inp)
+                C.append(a)
+
+for fl in os.listdir("../../../ALL/D/"):
+                inp = []
+                path = "../../../ALL/D/" + fl
+                txt = open(path,'r')
+                for line in txt:
+                        feature = line.split()[0]
+                        inp.append(feature)
+                a = np.array(inp)
+                D.append(a)
+
+for fl in os.listdir("../../../ALL/E/"):
+                inp = []
+                path = "../../../ALL/E/" + fl
+                txt = open(path,'r')
+                for line in txt:
+                        feature = line.split()[0]
+                        inp.append(feature)
+                a = np.array(inp)
+                E.append(a)
+
+A_ = []
+B_ = []
+C_ = []
+D_ = []
+E_ = []
+for x in A:
+	coeffs = wavedec(x,'db4',level=8)
+	A_.append(coeffs)
+	
+for x in B:
+	coeffs = wavedec(x,'db4',level=8)
+	B_.append(coeffs)
+	
+for x in C:
+	coeffs = wavedec(x,'db4',level=8)
+	C_.append(coeffs)
+for x in D:
+	coeffs = wavedec(x,'db4',level=8)
+	D_.append(coeffs)
+for x in E:
+	coeffs = wavedec(x,'db4',level=8)
+	E_.append(coeffs)
+
+a=[]
+b=[]
+c=[]
+d=[]
+e=[]
+y_a = []
+y_b = []
+y_c = []
+y_d = []
+y_e = []
+f=[]
+y_f=[]
+inputs = []
+outputs=[]
+minm = [1000000000000 for i in range(1,28)]
+maxm = [0 for i in range(1,28)]
+inp = []
+out = []
+for x in A_:
+	features = []
+	j=0
+	for y in x:
+		coef = np.array(y)
+		energy = np.sum(coef**2)
+		minm[j] = min(minm[j],energy)
+		maxm[j] = max(maxm[j],energy)
+		j=j+1
+		#approx_en = ap_entropy(coef,2,0.5)
+		#minm[j] = min(minm[j],approx_en)
+		#maxm[j] = max(maxm[j],approx_en)
+		#j=j+1
+		#samp_en = samp_entropy(coef,2,0.5)
+		#minm[j] = min(minm[j],samp_en)
+		#maxm[j] = max(maxm[j],samp_en)
+		#j=j+1
+		mean = np.mean(coef)
+		minm[j]= min(minm[j],mean)
+		maxm[j] = max(maxm[j],mean)
+		j=j+1
+		std = np.std(coef)
+		minm[j] = min(minm[j],std)
+		maxm[j] = max(maxm[j],std)
+		j=j+1
+		features.append(energy)
+		#features.append(approx_en)
+		#features.append(samp_en)
+		features.append(mean)
+		features.append(std)
+	a.append(features)
+	y_a.append(0)
+	inputs.append(features)
+	outputs.append("Class A")
+
+print("A done")
+
+for x in B_:
+        features = []
+	j=0
+        for y in x:
+                coef = np.array(y)
+                energy = np.sum(coef**2)
+		minm[j] = min(minm[j],energy)
+		maxm[j] = max(maxm[j],energy)
+                j=j+1
+                #approx_en = ap_entropy(coef,2,0.5)
+                #minm[j] = min(minm[j],approx_en)
+		#maxm[j] = max(maxm[j],approx_en)
+                #j=j+1
+                #samp_en = samp_entropy(coef,2,0.5)
+                #minm[j] = min(minm[j],samp_en)
+		#maxm[j] = max(maxm[j],samp_en)
+                #j=j+1
+                mean = np.mean(coef)
+                minm[j]= min(minm[j],mean)
+		maxm[j] = max(maxm[j],mean)
+                j=j+1
+                std = np.std(coef)
+                minm[j] = min(minm[j],std)
+		maxm[j]= max(maxm[j],std)
+                j=j+1
+
+                features.append(energy)
+                #features.append(approx_en)
+                #features.append(samp_en)
+                features.append(mean)
+                features.append(std)
+        b.append(features)
+	y_b.append(0)
+	inputs.append(features)
+	outputs.append("Class A")
+
+print("B done")
+for x in C_:
+        features = []
+	j=0
+        for y in x:
+                coef = np.array(y)
+                energy = np.sum(coef**2)
+		minm[j] = min(minm[j],energy)
+                maxm[j] = max(maxm[j],energy)
+                j=j+1
+                #approx_en = ap_entropy(coef,2,0.5)
+                #minm[j] = min(minm[j],approx_en)
+                #maxm[j] = max(maxm[j],approx_en)
+                #j=j+1
+                #samp_en = samp_entropy(coef,2,0.5)
+                #minm[j] = min(minm[j],samp_en)
+                #maxm[j] = max(maxm[j],samp_en)
+                #j=j+1
+                mean = np.mean(coef)
+                minm[j]= min(minm[j],mean)
+                maxm[j] = max(maxm[j],mean)
+                j=j+1
+                std = np.std(coef)
+                minm[j] = min(minm[j],std)
+                maxm[j]= max(maxm[j],std)
+                j=j+1
+
+
+                features.append(energy)
+                #features.append(approx_en)
+                #features.append(samp_en)
+                features.append(mean)
+                features.append(std)
+        c.append(features)
+	y_c.append(1)
+	inputs.append(features)
+	outputs.append("Class B")
+
+print("C done")
+for x in D_:
+        features = []
+	j=0
+        for y in x:
+                coef = np.array(y)
+                energy = np.sum(coef**2)
+		
+		minm[j] = min(minm[j],energy)
+                maxm[j] = max(maxm[j],energy)
+                j=j+1
+                #approx_en = ap_entropy(coef,2,0.5)
+                #minm[j] = min(minm[j],approx_en)
+                #maxm[j] = max(maxm[j],approx_en)
+                #j=j+1
+                #samp_en = samp_entropy(coef,2,0.5)
+                #minm[j] = min(minm[j],samp_en)
+                #maxm[j] = max(maxm[j],samp_en)
+                #j=j+1
+                mean = np.mean(coef)
+                minm[j]= min(minm[j],mean)
+                maxm[j] = max(maxm[j],mean)
+                j=j+1
+                std = np.std(coef)
+                minm[j] = min(minm[j],std)
+                maxm[j]= max(maxm[j],std)
+                j=j+1
+
+
+                features.append(energy)
+                #features.append(approx_en)
+                #features.append(samp_en)
+                features.append(mean)
+                features.append(std)
+        d.append(features)
+	y_d.append(1)
+	inputs.append(features)
+	outputs.append("Class B")
+
+print("D Done")
+
+for x in E_:
+        features = []
+	j=0
+        for y in x:
+                coef = np.array(y)
+                energy = np.sum(coef**2)
+			
+		minm[j] = min(minm[j],energy)
+                maxm[j] = max(maxm[j],energy)
+                j=j+1
+                #approx_en = ap_entropy(coef,2,0.5)
+                #minm[j] = min(minm[j],approx_en)
+                #maxm[j] = max(maxm[j],approx_en)
+                #j=j+1
+                #samp_en = samp_entropy(coef,2,0.5)
+                #minm[j] = min(minm[j],samp_en)
+                #maxm[j] = max(maxm[j],samp_en)
+                #j=j+1
+                mean = np.mean(coef)
+                minm[j]= min(minm[j],mean)
+                maxm[j] = max(maxm[j],mean)
+                j=j+1
+                std = np.std(coef)
+                minm[j] = min(minm[j],std)
+                maxm[j]= max(maxm[j],std)
+                j=j+1
+
+
+
+                features.append(energy)
+                #features.append(approx_en)
+                #features.append(samp_en)
+                features.append(mean)
+                features.append(std)
+        e.append(features)
+	f.append(features)
+	y_e.append(2)
+	y_f.append(2)
+	inputs.append(features)
+	inputs.append(features)
+	outputs.append("Class C")
+	outputs.append("Class C")
+print("E done")
+i=0
+while i < 100:
+	inp.append(a[i])
+	out.append(y_a[i])
+	inp.append(b[i])
+	out.append(y_b[i])
+	inp.append(c[i])
+	out.append(y_c[i])
+	inp.append(d[i])
+	out.append(y_d[i])
+	inp.append(e[i])
+	out.append(y_e[i])
+	inp.append(f[i])
+	out.append(y_f[i])
+	i=i+1
+
+print("done")
+i=0
+z= [i for i in range(1,len(e)+1)]
+import matplotlib.pyplot as plt
+"""i=0
+while i < 27:
+        j=0
+        p=[]
+        q=[]
+        r=[]
+        s=[]
+        t=[]
+        u=[]
+        while j < len(e):
+                p.append(a[j][i])
+                q.append(b[j][i])
+                r.append(c[j][i])
+                s.append(d[j][i])
+                t.append(e[j][i])
+                j=j+1
+        plt.plot(z,np.array(p))
+        plt.plot(z,np.array(q))
+        plt.plot(z,np.array(r))
+        plt.plot(z,np.array(s))
+        plt.plot(z,np.array(t))
+        plt.savefig("images/"+str(i)+"_out.jpg")
+	print("saved")
+        plt.clf()
+        i=i+1
+"""
+i=0
+for x in inputs:
+	j=0
+	for y in x:
+		den = maxm[j]-minm[j]
+		if den ==0:
+			den=np.amax(numpy.array(maxm)) - np.amin(numpy.array(minm))
+		inputs[i][j] = (inputs[i][j]- minm[j])/den
+		j=j+1
+	i=i+1
+i=0
+n=len(inputs)
+while i < n:
+	inputs.append(inputs[i])
+	outputs.append(outputs[i])
+	i=i+1
+arr = [ i for i in range(0,len(inputs))]
+arr = np.random.permutation(arr)
+new_inputs = []
+new_outputs = []
+for x in arr:
+	new_inputs.append(inputs[x])
+	new_outputs.append(outputs[x])
+
+new_outputs = np.asarray(new_outputs)
+rfc = RandomForestClassifier(n_estimators=1000)
+rfc.fit(new_inputs,new_outputs)
+from sklearn.cross_validation import cross_val_score
+arr = [ i for i in range(0,len(inputs))]
+arr = np.random.permutation(arr)
+new_inputs = []
+new_outputs = []
+for x in arr:
+        new_inputs.append(inputs[x])
+        new_outputs.append(outputs[x])
+new_outputs = np.asarray(new_outputs)
+print("cross validating")
+scores = cross_val_score(rfc,new_inputs,new_outputs,cv=100)
+print("Accuracy: %0.2f (+/-%0.2f)" %(scores.mean(),scores.std()*2))
+#import ipdb;ipdb.set_trace()
+inputs1 = new_inputs[0:1*(len(new_inputs)/2)]
+outputs1 = new_outputs[0:1*(len(new_inputs)/2)]
+inputs2 = new_inputs
+outputs2 = new_outputs
+outputs2 = np.asarray(outputs2)
+rfc = RandomForestClassifier(n_estimators=400)
+rfc.fit(inputs2,outputs2)
+from sklearn.cross_validation import cross_val_score
+arr = [ i for i in range(0,len(inputs1))]
+arr = np.random.permutation(arr)
+new_inputs = []
+new_outputs = []
+for x in arr:
+        new_inputs.append(inputs1[x])
+        new_outputs.append(outputs1[x])
+new_outputs = np.asarray(new_outputs)
+print("cross validating again")
+scores = cross_val_score(rfc,new_inputs,new_outputs,cv=100)
+
+print("Accuracy: %0.2f (+/-%0.2f)" %(scores.mean(),scores.std()*2))
+y_pred_rf = rfc.predict_proba(inputs2)[:, 1]
+fpr_rf, tpr_rf, _ = roc_curve(outputs2, y_pred_rf)
+from sklearn.learning_curve import learning_curve
+import matplotlib.pyplot as plt
+plt.plot(fpr_rf,tpr_rf,label='RF')
+plt.xlabel('False positive rate')
+plt.ylabel('True positive rate')
+plt.title('ROC curve (zoomed in at top left)')
+#plt.legend(loc='best')
+plt.savefig("ROC_random_forest_classifier.png")
+plt.clf()
+
+train_sizes, train_scores, test_scores = learning_curve(
+        rfc, new_inputs, new_outputs, n_jobs=-1,cv=10, train_sizes=np.linspace(.1, 1., 10), verbose=0)
+ 
+train_scores_mean = np.mean(train_scores, axis=1)
+train_scores_std = np.std(train_scores, axis=1)
+test_scores_mean = np.mean(test_scores, axis=1)
+test_scores_std = np.std(test_scores, axis=1)
+ 
+plt.figure()
+plt.title("RandomForestClassifier Test Scores")
+plt.legend(loc="best")
+plt.xlabel("Training examples")
+plt.ylabel("Score")
+plt.ylim((0.6, 1.01))
+plt.gca().invert_yaxis()
+plt.grid()
+ 
+#train size=10 cv=10
+# Plot the average training and test score lines at each training set size
+#plt.plot(train_sizes, train_scores_mean, 'o-', color="b", label="Training score")
+plt.plot(train_sizes, test_scores_mean, 'o-', color="r", label="Test score")
+ 
+# Plot the std deviation as a transparent range at each training set size
+#plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, 
+#                 alpha=0.1, color="b")
+plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, 
+                 alpha=0.1, color="r")
+# Draw the plot and reset the y-axis
+plt.draw()
+#plt.show()
+plt.gca().invert_yaxis()
+#plt.legend()
+plt.savefig("images/Random_Forest_Classifier_result1.png")
+plt.clf()	
+#P,Q,R,S,T,U = create_training_set(inp,out,minm,maxm)
+#mlp(P,Q,R,S,T,U)
+