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How to use test_sequence method in molecule

Best Python code snippet using molecule_python

fetch_HMM.py

Source:fetch_HMM.py

1# Fetch the data from Firebase2from firebase import firebase3import math4firebase = firebase.FirebaseApplication('https://androidbletutorial.firebaseio.com/',None)5All_Sample = []6result=firebase.get('',None)7length = 108# Define Partition Function9def Partition(value):10    end_value = pow(2,16)-1  # maximam value11    global length12    num_level = length13    return_value = math.ceil(value*num_level/end_value)14    #print(value,"  ", return_value);15    return return_value16# Sample value17for key in result.keys():18  temp = result[key]["text"].split(":")[7]19  x1, x2 = temp.split(" ")[1], temp.split(" ")[2]20  y1, y2 = temp.split(" ")[3], temp.split(" ")[4]21  z1, z2 = temp.split(" ")[5], temp.split(" ")[6]22  x = int(x1) + int(x2)<<823  y = int(y1) + int(y2)<<824  z = int(z1) + int(z2)<<825  sample = math.sqrt(x*x+y*y+z*z)26  sample = Partition(int(sample))27  All_Sample.append(sample)28Sample_list = ''.join([str(elem) for elem in All_Sample]) 29print(Sample_list)30# find transition matrix and emission matrix31#defining states and sequence symbols32import numpy as np33import decimal34from decimal import Decimal35import math36import time37#transition matrix38transition = np.array([[0.9,0.1],[0.1,0.9]])39#emission matrix40emission = np.array([[0.05,0.05,0.05,0.05,0.1,0.1,0.1,0.1,0.2,0.2],41                     [0.2,0.2,0.1,0.1,0.1,0.1,0.05,0.05,0.05,0.05]])42states = ['H','C']43states_dic = {'H':0, 'C':1}44#sequence_syms = {'1':0,'6600':1,'14000':2,'28000':3,'35000':4,'41000':5,'47000':6,'55000':7,'57000':8,'65000':9}45sequence_syms = {'0':0,'1':1,'2':2,'3':3,'4':4,'5':5,'6':6,'7':7,'8':8,'9':9}46sequence = ['0','1','2','3','4','5','6','7','8','9']47#sequence = ['1', '6600', '14000', '28000', '35000', '41000', '47000', '55000', '57000', '65000']48#test sequence49test_sequence = Sample_list50#test_sequence = ['47000','35000','65000','65000','41000','55000','57000','65000','47000','55000']51test_sequence = test_sequence52#probabilities of going to end state53end_probs = [0.1, 0.1]54#probabilities of going from start state55start_probs = [0.5, 0.5]56#function to find forward probabilities57def forward_probs():58    # node values stored during forward algorithm59    node_values_fwd = np.zeros((len(states), len(test_sequence)))60    for i, sequence_val in enumerate(test_sequence):61        for j in range(len(states)):62            # if first sequence value then do this63            if (i == 0):64                node_values_fwd[j, i] = start_probs[j] * emission[j, sequence_syms[sequence_val]]65            # else perform this66            else:67                values = [node_values_fwd[k, i - 1] * emission[j, sequence_syms[sequence_val]] * transition[k, j] for k in68                          range(len(states))]69                node_values_fwd[j, i] = sum(values)70    #end state value71    end_state = np.multiply(node_values_fwd[:, -1], end_probs)72    end_state_val = sum(end_state)73    return node_values_fwd, end_state_val74#function to find backward probabilities75def backward_probs():76    # node values stored during forward algorithm77    node_values_bwd = np.zeros((len(states), len(test_sequence)))78    #for i, sequence_val in enumerate(test_sequence):79    for i in range(1,len(test_sequence)+1):80        for j in range(len(states)):81            # if first sequence value then do this82            if (-i == -1):83                node_values_bwd[j, -i] = end_probs[j]84            # else perform this85            else:86                values = [node_values_bwd[k, -i+1] * emission[k, sequence_syms[test_sequence[-i+1]]] * transition[j, k] for k in range(len(states))]87                node_values_bwd[j, -i] = sum(values)88    #start state value89    start_state = [node_values_bwd[m,0] * emission[m, sequence_syms[test_sequence[0]]] for m in range(len(states))]90    start_state = np.multiply(start_state, start_probs)91    start_state_val = sum(start_state)92    return node_values_bwd, start_state_val93#function to find si probabilities94def si_probs(forward, backward, forward_val):95    si_probabilities = np.zeros((len(states), len(test_sequence)-1, len(states)))96    for i in range(len(test_sequence)-1):97        for j in range(len(states)):98            for k in range(len(states)):99                si_probabilities[j,i,k] = ( forward[j,i] * backward[k,i+1] * transition[j,k] * emission[k,sequence_syms[test_sequence[i+1]]] ) \100                                                    / forward_val101    return si_probabilities102#function to find gamma probabilities103def gamma_probs(forward, backward, forward_val):104    gamma_probabilities = np.zeros((len(states), len(test_sequence)))105    for i in range(len(test_sequence)):106        for j in range(len(states)):107            #gamma_probabilities[j,i] = ( forward[j,i] * backward[j,i] * emission[j,sequence_syms[test_sequence[i]]] ) / forward_val108            gamma_probabilities[j, i] = (forward[j, i] * backward[j, i]) / forward_val109    return gamma_probabilities110#performing iterations until convergence111for iteration in range(2000):112    print('\nIteration No: ', iteration + 1)113    # print('\nTransition:\n ', transition)114    # print('\nEmission: \n', emission)115    #Calling probability functions to calculate all probabilities116    fwd_probs, fwd_val = forward_probs()117    bwd_probs, bwd_val = backward_probs()118    si_probabilities = si_probs(fwd_probs, bwd_probs, fwd_val)119    gamma_probabilities = gamma_probs(fwd_probs, bwd_probs, fwd_val)120    # print('Forward Probs:')121    # print(np.matrix(fwd_probs))122    123    # print('Backward Probs:')124    # print(np.matrix(bwd_probs))125    #126    # print('Si Probs:')127    # print(si_probabilities)128    # print('Gamma Probs:')129    # print(np.matrix(gamma_probabilities))130    #caclculating 'a' and 'b' matrices131    a = np.zeros((len(states), len(states)))132    b = np.zeros((len(states), len(sequence_syms)))133    #'a' matrix134    for j in range(len(states)):135        for i in range(len(states)):136            for t in range(len(test_sequence)-1):137                a[j,i] = a[j,i] + si_probabilities[j,t,i]138            denomenator_a = [si_probabilities[j, t_x, i_x] for t_x in range(len(test_sequence) - 1) for i_x in range(len(states))]139            denomenator_a = sum(denomenator_a)140            if (denomenator_a == 0):141                a[j,i] = 0142            else:143                a[j,i] = a[j,i]/denomenator_a144    #'b' matrix145    for j in range(len(states)): #states146        for i in range(len(sequence)): #seq147            indices = [idx for idx, val in enumerate(test_sequence) if val == sequence[i]]148            numerator_b = sum( gamma_probabilities[j,indices] )149            denomenator_b = sum( gamma_probabilities[j,:] )150            if (denomenator_b == 0):151                b[j,i] = 0152            else:153                b[j, i] = numerator_b / denomenator_b154    print('\nMatrix a:\n')155    print(np.matrix(a.round(decimals=4)))156    print('\nMatrix b:\n')157    print(np.matrix(b.round(decimals=4)))158    transition = a159    emission = b160    new_fwd_temp, new_fwd_temp_val = forward_probs()161    print('New forward probability: ', new_fwd_temp_val)162    diff =  np.abs(fwd_val - new_fwd_temp_val)163    print('Difference in forward probability: ', diff)164    if (diff < 0.000000001):...

classify_nn.py

Source:classify_nn.py

1import operator2import math3import numpy as np4def distance(A, B):5	sum_ = 06	for i in range(len(A)):7		sum_ += (A[i]-B[i])**28	return sum_9def find_L2(sequence, test_sequence):10	assert(len(sequence.frames) == len(test_sequence.frames))11	dist = 012	for i in range(len(sequence.frames)):13		sframe = sequence.frames[i].frame14		tframe = test_sequence.frames[i].frame15		dist += distance(sequence.frames[i].frame, test_sequence.frames[i].frame)16	return dist17def classify_nn(test_sequence, training_gesture_sets):18	"""19	Classify test_sequence using nearest neighbors20	:param test_gesture: Sequence to classify21	:param training_gesture_sets: training set of labeled gestures22	:return: a classification label (an integer between 0 and 8)23	"""24	min_dist = float('inf')25	result = None26	for gs in training_gesture_sets:27		total = 028		for seq in gs.sequences:29			l2 = find_L2(seq, test_sequence)30			total += l231		avg = total/len(gs.sequences)32		if avg < min_dist:33			min_dist = avg34			result = gs.label...

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