def process_image(img):
# Loader.print_image(img)
# print("[DEBUG] Showing VISUAL denoising algorithm comparison")
# denoising_comparison(img)
# print("[DEBUG] Showing HISTOGRAM denoising algorithm comparison")
# denoising_comparison(img, True)
# DENOISING IMAGE
denoised_img = smooth.median_filter(img, 9)
denoised_img = smooth.median_filter(denoised_img, 7)
# PRINT DENOISED IMAGE AND HISTOGRAM
#Loader.print_image(denoised_img)
# Loader.hist_and_cumsum(denoised_img)
# thresholding_comparison(denoised_img)
th_img = thresh.apply_thresholding_algorithm(denoised_img,
thresh.THRESH_TRIANGLE)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 5))
stretched = cv2.morphologyEx(th_img, cv2.MORPH_ERODE, kernel)
back, front = thresh.get_regions(denoised_img, stretched)
# Loader.hist_compare([back, front], ["Back", "Front"])
#Loader.print_image(front)
eq = Loader.equalization(front.astype("uint8"))
eq = bright_and_contrast(eq, 2.8, 80)
eq = smooth.gaussian(eq, 2.5)
Loader.print_image(eq)
# Loader.hist_and_cumsum(eq)
# EDGE DETECTION
#edgesFunctions(eq) # Comparison of different edge detection method
edges = edg.laplacian_of_gaussian(eq, 2)
Loader.print_image(edges)
# Fill the cornea area with white pixels
dilated = fill_cornea(edges)
#Loader.print_image(dilated)
#Calculate distances in the cornea-lens region
#lineas = dw.find_vertical_lines(dilated)
#diferencias,posiciones = dw.calculate_differences(lineas)
#dw.draw_graph_distance(diferencias, posiciones)
#output_image = dw.lines_image(lineas, img)
# Surround the córnea area and lens edges with visible and thin line
(i, contornos, jerarquia) = cv2.findContours(dilated, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = []
for c in contornos:
if cv2.contourArea(c) < 1000:
continue
else:
cnts.append(c)
cv2.drawContours(img, cnts, -1, (0, 0, 255), 3)
#Loader.print_image(img)
return img
def PowerSpectrum(data): # power spectrum smooth_data = SmoothFunction(data) freqs = numpy.array(numpy.fft.fftfreq(n=len(data), d=dt))/c fft = numpy.array(numpy.fft.fft(smooth_data)) fft = fft * freqs spectrum = abs(fft) * abs(fft) smooth_spectrum = Smoothing.window_smooth(spectrum, window='hamming', window_len=15) return (freqs,spectrum,smooth_spectrum)
def crossValidationLinearInterpolation(tweetList, k, maxNgram):
for i in xrange(k):
trainSet, testSet = divideDataset(tweetList, k, i)
trainDist, arrayLanguages, languagesAll = utils.obtainNgrams(trainSet, maxNgram)
linearCoefficients = linear.getlinearcoefficientsForLanguageArray(arrayLanguages, maxNgram, trainDist)
print linearCoefficients
count = 0
tot = 0
for tweet in testSet:
predictedLanguage, probability = linear.getPredictedLanguageForTweet(linearCoefficients, tweet.text, maxNgram, trainDist)
utils.printResultTXT(predictedLanguage, tweet)
if(predictedLanguage == tweet.language):
count = count + 1;
tot = tot +1
# print str(count)+'/'+str(tot)
print 'correct tweets fold '+str(i)+' = '+str(count)+'/'+str(tot)
def fill_cornea(edge_image):
k1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 10))
res1 = cv2.morphologyEx(edge_image, cv2.MORPH_DILATE, k1)
print("DILATED")
#Loader.print_image(res1)
enhance_black = smooth.min_filter(res1, 7)
print("BLACk")
#Loader.print_image(enhance_black)
k2 = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 5))
res2 = cv2.morphologyEx(enhance_black, cv2.MORPH_ERODE, k2)
print("eroded")
#Loader.print_image(res2)
res3 = smooth.max_filter(res2, 3)
res3 = smooth.gaussian(res3, 1.5)
print("dilated")
#Loader.print_image(res3)
return res3
def PlotSpans (files, axs, clr): cdfs = [CDF(f) for f in files] data = [c[0] for c in cdfs] data = reduce(operator.add, data) hist,edges = numpy.histogram(data, range=(0.0,400.0), bins=200) hist = Smoothing.window_smooth(hist) if clr == 'b': plt.bar([i-width/2 for i in range(len(new_bins[:-1]))], hist, width, color=clr, align='center') else: plt.bar([i+width/2 for i in range(len(new_bins[:-1]))], hist, width, color=clr, align='center')
def getLifespan(file,axs=None): cdf = CDF(file) # raw data data = cdf[0] # smooth out the data and digitize above a certain threshold smooth = Smoothing.window_smooth(numpy.array(data), window_len=40, window='blackman') smooth = smooth - 0.18 new_data = numpy.ceil(smooth) smooth = smooth + 0.18 if axs: axs.plot(data[1200:2800], linewidth=2.5, color='k', linestyle=':', alpha=0.7, label=r'$C(t)$') axs.plot(smooth[1200:2800], linewidth=2.0, color='r', linestyle='-', alpha=0.7, label=r'$C_s(t)$') axs.plot(new_data[1200:2800], linewidth=3.5, color='g', linestyle='-', alpha=0.8, label=r'$f(t)$') return [c for c in CutUpCycleBits(new_data)]
def denoising_comparison(input_image, hist: bool = False):
# NON LOCAL MEAN DENOISING
nl_means_denoised_img = smooth.denoising_NlMeans(input_image)
# MEDIAN FILTER DENOISING
mean_denoised_img = smooth.median_filter(input_image, 9)
mean_denoised_img = smooth.median_filter(input_image, 9)
# GAUSSIAN DENOISING
gaussian_denoised = smooth.gaussian(input_image, 1.5)
# MINIMUM FILTER
minimum_denoised = smooth.min_filter(input_image, (5, 5))
# MAXIMUM FILTER
maximum_denoised = smooth.max_filter(input_image, (5, 5))
denoised_imgs = [
img, gaussian_denoised, mean_denoised_img, nl_means_denoised_img,
minimum_denoised, maximum_denoised
]
denoised_titles = [
"Original", "Denoised Gaussian", "Median Filtered", "NL Means Filter",
"Minimums Filter", "Maximums Filter"
]
Loader.hist_compare(denoised_imgs, denoised_titles, hist)
return denoised_imgs, denoised_titles
def plot(x,y,wanted_list,groups):
#print(x[0:5])
#one graph for each group
font = {'size': 13}
plt.rc('font', **font)
stevec = 0
for g in groups:
#if len(g) == 1: plt.plot(x, y[a])
#else:
for a in g:
#plt.plot(x,y[a], label=wanted_list[a])
yy = [1000000 * m for m in Smoothing.smoothTriangle(y[a],3)]
el = yy[0]
yy = [el,el,el] + yy[:-3]
#yy = yy[3:-3]
#word = wanted_list[a][0].title() + ' ' + wanted_list[a][1].title()
word = wanted_list[a].title()
#if word == 'Dicaprio': word ='DiCaprio'
#if wanted_list[a][1]=='hiv': word = 'Virus HIV'
#plt.plot(x,yy,label=wanted_list[a].title())
yy = yy[6:-10]
xx = x[6:-10]
print('lenx',len(x),'len y',len(yy))
plt.plot(xx,yy,label = word)
#plt.legend(bbox_to_anchor=(1, 1), loc=0, borderaxespad=1.)
plt.legend(loc=1)
if stevec==0:
plt.text(datetime.datetime(2004, 8, 13, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2004, 8, 13, 0, 0), -110, 'OI Atene',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.text(datetime.datetime(2000, 9, 15, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2000, 9, 15, 0, 0), -110, 'OI Sydney',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.text(datetime.datetime(1998, 2, 7, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(1998, 2, 7, 0, 0), -110, 'OI Nagano',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.text(datetime.datetime(2006, 2, 10, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2006, 2, 10, 0, 0), -110, 'OI Torino',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.text(datetime.datetime(2002, 2, 8, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2002, 2, 8, 0, 0), -110, 'OI Salt Lake City',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.xlabel('Leto',labelpad=70)
elif stevec==1:
plt.text(datetime.datetime(2001, 1, 20, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2001, 1, 20, 0, 0), -80, 'Izvolitev Busha',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.xlabel('Leto',labelpad=80)
elif stevec==3:
plt.text(datetime.datetime(2003, 3, 20, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2003, 3, 20, 0, 0), -310, 'Invazija na Irak',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.text(datetime.datetime(2001, 10, 7, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2001, 10, 7, 0, 0), -310, 'Invazija na Afganistan',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.xlabel('Leto',labelpad=100)
elif stevec==20:
plt.text(datetime.datetime(2001, 11, 23, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2001, 11, 23, 0, 0), -75, 'Primer BSE v SLO',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.text(datetime.datetime(2006, 2, 12, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2006, 2, 12, 0, 0), -75, 'Primer ptičje gripe v SLO ',horizontalalignment='right', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.xlabel('Leto',labelpad=75)
elif stevec==6:
plt.text(datetime.datetime(2001, 9, 11, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2001, 9, 11, 0, 0), -110, '11. september',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.text(datetime.datetime(2004, 9, 3, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2004, 9, 3, 0, 0), -145, 'Beslan',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.text(datetime.datetime(2004, 3, 11, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2004, 3, 11, 0, 0), -110, 'Madrid',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.text(datetime.datetime(2005, 7, 7, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2005, 7, 7, 0, 0), -110, 'London',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.text(datetime.datetime(2002, 10, 23, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2002, 10, 23, 0, 0), -145, 'Moskva',horizontalalignment='left', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.text(datetime.datetime(2002, 10, 12, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(2002, 10, 12, 0, 0), -110, 'Bali',horizontalalignment='right', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.text(datetime.datetime(1998, 8, 7, 0, 0), 0, ' |', fontsize=15,weight='heavy')
plt.text(datetime.datetime(1998, 8, 7, 0, 0), -110, 'Tanzanija',horizontalalignment='center', bbox=dict(facecolor='blue', alpha=0.1),size=23,style='italic',weight='light')
plt.xlabel('Leto',labelpad=130)
else:
plt.xlabel('Leto',labelpad=0)
#if stevec==4: plt.legend(loc=2)
#else: plt.legend(loc=1)
stevec+=1
plt.gcf().autofmt_xdate()
plt.ylabel('Število pojavitev na milijon besed',labelpad=50)
#plt.xlabel('Leto',labelpad=20)
plt.show()
tweetListPreProcessed_test = preprocess.main(tweetList_test)
# shuffle(tweetListPreProcessed)
# 3-. Algorithms
# 3.1-. Algorithms: Bayesian Networks
# 3.2.1-. Linear interpolation
# Generate linear coefficients: input (n-grams and language)
# Smooth data
# cv.crossValidationLinearInterpolation(tweetListPreProcessed_train, 3, maxNgram)
linearCoefficientsAll = list()
trainDist, arrayLanguages, languagesAll = utils.obtainNgrams(tweetListPreProcessed_train, maxNgram)
for gram in xrange(1, maxNgram+1):
linearCoefficientsAll.append(linear.getlinearcoefficientsForLanguageArray(arrayLanguages, gram, trainDist))
print linearCoefficientsAll
# linearCoefficientsALL = read.readLinearCoefficients(LI_Coefficients)
count = 4 # Desde que gram empezar
for i in xrange(count, maxNgram):
count = count + 1
t0 = time.time()
for tweet in tweetListPreProcessed_test:
# t0 = time.time()
predictedLanguage, probability = linear.getPredictedLanguageForTweet(linearCoefficientsAll[i], tweet.text, count,
return outputCanvas
if __name__ == '__main__':
print("[DEBUG] Load image from local sources")
# img = Loader.load_image("test/th.jpeg")
# Loader.print_image(stretched)
# Loader.print_image(smoothed_thresholded)
# Loader.print_image(front)
for i in np.arange(1, 13):
img = Loader.load_image("im" + i.__str__() + ".jpeg")
print("Loaded image " + "im" + i.__str__() + ".jpeg")
# DENOISING IMAGE
denoised_img = smooth.median_filter(img, 9)
denoised_img = smooth.median_filter(denoised_img, 7)
# thresholding_comparison(denoised_img)
th_img = thresh.apply_thresholding_algorithm(denoised_img,
thresh.THRESH_TRIANGLE)
#Suavizamos los bordes marcados por la segmentacion
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 5))
stretched = cv2.morphologyEx(th_img, cv2.MORPH_ERODE, kernel)
smoothed_thresholded = smooth.max_filter(stretched, 5)
#Obtenemos las areas correspondientes a la imagen original, despues del segmentado
back, front = thresh.get_regions(denoised_img, stretched)
IMAGE_TYPE_CLASSIFICATION = define_image_properties(
smoothed_thresholded)
if IMAGE_TYPE_CLASSIFICATION == "A":
c = '"En Cada Lucha Aquel Que Va A Muerte Es El Que Gana" Goazen @PasaiaRegional!! #aupaekipo #aupapasaia pic.twitter.com/BQ1ikdE2Qt'
text = preprocess.preprocessText(tweetEU)
# linearCoefficients = linear.getlinearcoefficientsForLanguageArray(arrayLanguages, maxNgram, corpusNgrams)
linearCoefficientsALL = read.readLinearCoefficients(LI_Coefficients)
linearCoefficients = linearCoefficientsALL[maxNgram-1]
import time
t1 = time.time()
for tweet in tweetListPreProcessedtest:
t0 = time.time()
predictedLanguage, probability = linear.getPredictedLanguageForTweet(linearCoefficients, tweet.text, maxNgram, corpusNgrams)
utils.printResultTXT(predictedLanguage, tweet, 5)
print "time for tweet= "+str(time.time()-t0)
print "time total= "+str(time.time()-t1)
# sys.stdout.write("\n Tweet: "+str(text.encode("utf-8")))
# sys.stdout.write("\n Tweet language: "+str(predictedLanguage)+"\n Probability of: "+str(probability)+"\n")
# 3.3-. Algorithms: Ranking Methods
# cv.nestedCrossValidation(tweetListPreProcessed,5,5,[0,0,0],arrayLanguagesFull)
# cv.crossValidation(tweetListPreProcessed, 3, [0,0,0], arrayLanguagesFull, maxNgram+1)
# 3.4-. Out-of-place Measure
# _____________________________________________________________________________
# 1-. Read dataset and create tweetList fullfilled of Tweet object*
dataset = sys.argv[1]
maxNgram = int(sys.argv[2])
filename = os.path.basename(dataset).split('.')
tweetList = read.read_tweets_dataset(dataset)
# 2-. Pre-process state
# Raw data -> tweetList
# Clean data -> tweetListPreProcessed
tweetListPreProcessed = preprocess.main(tweetList)
# 3-. OBTAIN N-GRAMS and Linear Coefficients
for i in xrange(5, maxNgram+1):
corpusNgrams, arrayLanguages,arrayLanguagesFull = utils.obtainNgrams(tweetListPreProcessed, i+1)
linearCoefficients = linear.getlinearcoefficientsForLanguageArray(arrayLanguages, i, corpusNgrams)
# print linearCoefficients
file = open('../Dataset/LICoefficients_'+str(maxNgram)+'gram_for-'+str(filename[0])+'.txt', 'a+')
for li in linearCoefficients:
file.write(str(i)+"\t"+str(li[0]))
for co in xrange(1, i+1):
file.write("\t"+str(li[co]))
file.write("\n")
file.close()
fig = plt.figure(num=None, facecolor='w', edgecolor='w', frameon=True)
axs = fig.add_subplot(1,1,1)
axs.set_ylabel(r'Spectrum', size='xx-large')
axs.set_xlabel(r'Frequency / cm$^{-1}$', size='xx-large')
# use the same axis for all the spectra
freq_axis = FreqAxis(len(tcfs[0])/2,dt)
fft_tcfs_x = [FFT(t) for t in tcfs_x]
fft_tcfs_y = [FFT(t) for t in tcfs_y]
fft_tcfs_z = [FFT(t) for t in tcfs_z]
fft_tcfs = [(x+y+z)/3.0 for x,y,z in zip(fft_tcfs_x,fft_tcfs_y,fft_tcfs_z)]
#avg_fft = reduce(operator.add, fft_tcfs_z)/len(fft_tcfs_z)
avg_fft = FFT(avg_tcf)
smooth = Smoothing.window_smooth(avg_fft)
#for f in fft_tcfs:
#axs.plot(freq_axis, f, linewidth=2.0)
axs.plot(freq_axis, smooth, linewidth=4.0, linestyle='-', color='k')
vector_tcf_ffts = [FFT(t) for t in vector_tcfs]
avg_vector_fft = numpy.array(reduce(operator.add,vector_tcf_ffts))/len(vector_tcf_ffts)
smooth_vector_fft = Smoothing.window_smooth(avg_vector_fft)
vector_freq_axis = FreqAxis(correlation_tau,dt)
print len(vector_freq_axis)
print len(smooth_vector_fft)
axs.plot(vector_freq_axis, smooth_vector_fft, linewidth=4.0, linestyle='-', color='r')
b = 'Hau ez dakit zer den estamos hablando en un idioma edo beste batean'
c = '"En Cada Lucha Aquel Que Va A Muerte Es El Que Gana" Goazen @PasaiaRegional!! #aupaekipo #aupapasaia pic.twitter.com/BQ1ikdE2Qt'
text = preprocess.preprocessText(tweetEU)
# linearCoefficients = linear.getlinearcoefficientsForLanguageArray(arrayLanguages, maxNgram, corpusNgrams)
linearCoefficientsALL = read.readLinearCoefficients(LI_Coefficients)
linearCoefficients = linearCoefficientsALL[maxNgram - 1]
import time
t1 = time.time()
for tweet in tweetListPreProcessedtest:
t0 = time.time()
predictedLanguage, probability = linear.getPredictedLanguageForTweet(
linearCoefficients, tweet.text, maxNgram, corpusNgrams)
utils.printResultTXT(predictedLanguage, tweet, 5)
print "time for tweet= " + str(time.time() - t0)
print "time total= " + str(time.time() - t1)
# sys.stdout.write("\n Tweet: "+str(text.encode("utf-8")))
# sys.stdout.write("\n Tweet language: "+str(predictedLanguage)+"\n Probability of: "+str(probability)+"\n")
# 3.3-. Algorithms: Ranking Methods
# cv.nestedCrossValidation(tweetListPreProcessed,5,5,[0,0,0],arrayLanguagesFull)
# cv.crossValidation(tweetListPreProcessed, 3, [0,0,0], arrayLanguagesFull, maxNgram+1)
# 3.4-. Out-of-place Measure
def fetch_output(training_file, dev_set):
# Defines
tags_file = r'POSTagList.txt'
delimiter = '\t'
word_column = 1
tag_column = 2
# Initialization
answers = []
# Parsing sentences from training data and generating word-tag bigrams
tag_frequency_count = CountFrequency.give_freq_counts(
training_file, delimiter, tag_column)
word_frequency_count = CountFrequency.give_freq_counts(
training_file, delimiter, word_column)
sentence_seq_word_list = TPM.construct_sentence_sequence(
training_file, delimiter, word_column, 0)
sentence_seq_tag_list = TPM.construct_sentence_sequence(
training_file, delimiter, tag_column, 0)
unked_sequence_word_list = Input_Generation.define_training_unk_words(
word_frequency_count, sentence_seq_word_list)
word_tag_pairs = EPM.get_epm_bigrams(sentence_seq_tag_list,
unked_sequence_word_list)
tag_tag_pairs = TPM.get_bigrams(sentence_seq_tag_list)
vocabulary = set(unked_sequence_word_list)
# Creating the master parameter list
# master_a = Smoothing.get_backoff_smoothed_tpm(tags_file, tag_tag_pairs, tag_frequency_count)
master_a = Smoothing.get_add_k_smoothed_tpm(tags_file, tag_tag_pairs,
tag_frequency_count)
# master_a = TPM.get_transition_probability_matrix(tags_file, tag_tag_pairs, tag_frequency_count)
master_b = EPM.get_emission_probability_matrix(tags_file, vocabulary,
word_tag_pairs,
tag_frequency_count)
master_pie_1 = TPM.get_initial_pi_matrix(tags_file, tag_tag_pairs,
unked_sequence_word_list)
master_pie_2 = TPM.get_end_pi_matrix(tags_file, tag_tag_pairs,
tag_frequency_count)
# Apply smoothing to Transition probability matrix
# Generating the list of sentences to be fed
all_inputs = TPM.construct_sentence_sequence(dev_set, delimiter, 1, 0)
# Find out the state_sequence_list and observation_sequence_list
extracted_inputs = Input_Generation.extract_input_sentences_list(
all_inputs)
unked_extracted_inputs = Input_Generation.define_extracted_unk_words(
unked_sequence_word_list, extracted_inputs)
#extracted_tags = Input_Generation.extract_possible_tags_list(extracted_inputs, word_tag_pairs)
# loop this in for all sentences
for index, observation_sequence in enumerate(unked_extracted_inputs):
if len(observation_sequence):
state_sequence = ['I', 'O', 'B']
# construct matrix A
a = LocalParamters.construct_local_transition(
state_sequence, master_a)
# construct matrix B
b = LocalParamters.construct_local_emission(
state_sequence, observation_sequence, vocabulary, master_b)
# construct matrix pie_1
pie_1 = LocalParamters.construct_local_pie_start(
state_sequence, master_pie_1)
# construct matrix pie_2
pie_2 = LocalParamters.construct_local_pie_end(
state_sequence, master_pie_2)
# input it to viterbi
answer_string = ViterbiDecoding.viterbi_decode(
observation_sequence, state_sequence, a, b, pie_1, pie_2)
# fetch the answer strings
answers.extend(answer_string)
answers.extend(" ")
return answers
#import data #specify sim run dataDM = np.loadtxt(dmType+'_dm.txt',skiprows=1) # In[5]: #cut function call cutDataMin,radMin,rMinSearch,cutDataMax,radMax,rMaxSearch = Cuts(dataDM) # In[6]: #smoothing function call hMin,hMax = Smoothing(cutDataMin,cutDataMax,rMinSearch,rMaxSearch) # In[7]: #density function call rhoMin,rhoMax = Densities(kernel, cutDataMin, hMin, cutDataMax, hMax) # In[8]: #density gradient and laplacian function call del_rhoMin, delsq_rhoMin, del_rhoMax, delsq_rhoMax = Gradients(del_kernel,delsq_kernel,cutDataMin,rhoMin, cutDataMax,rhoMax,hMin,hMax) # In[9]:
