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

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(1,37)]

maxm = [0 for i in range(1,37)]

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)

importances = rfc.feature_importances_

import ipdb; ipdb.set_trace()

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[0:3*(len(new_inputs)/4)]

outputs1 = new_outputs[0:3*(len(new_inputs)/4)]

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)

test = open("results2.txt",'w')

for x in scores:

    test.write(str(x)+ "\n")

import ipdb; ipdb.set_trace()

test.close()

print("Accuracy: %0.2f (+/-%0.2f)" %(scores.mean(),scores.std()*2))

#P,Q,R,S,T,U = create_training_set(inp,out,minm,maxm)

#mlp(P,Q,R,S,T,U)