def case_function(samples, obj): sam = Samples() # object required to plot num_bin_x, num_bin_y = (bin_x.get(), bin_y.get()) mi_text.set(mi_text.get() + '\nCalculating...') not_selected = True if plot_sample.get(): sam.plot_sample(samples, num_bin_x, num_bin_y) not_selected = False if plot_compare.get(): obj.plot_compare2(num_bin_x, num_bin_y, noise.get(), step.get(), significance.get()) not_selected = False if plot_proposal.get(): sam.plot_propose(samples, num_bin_x, step.get()) not_selected = False if plot_partition.get(): sam.plot_partition(samples, num_bin_x, num_bin_y) not_selected = False if plot_converge_in_samples.get(): converge_in_samples(num_bin_x, num_bin_y, obj) not_selected = False if samples_check.get(): samples_grid(num_bin_x, num_bin_y) not_selected = False if bin_selection.get(): bin_selection_validation(sigma=significance.get(), obj=obj) not_selected = False if cal_measures.get(): calculate_measures(samples, num_bin_x, num_bin_y) not_selected = False if not_selected: mi_text.set('Select a plot or calculation')
def case_function(samples, obj): sam = Samples() print(samples) numBinx, numBiny = Binx.get(), Biny.get() #numBinx = 30 #numBiny = 30 print(numBinx, numBiny) print(numBinx) x, y = samples[:, 0], samples[:, 1] MI = mutual_info(x, y, numBinx, numBiny) ex = entropy(x, numBinx) ey = entropy(y, numBiny) R = norm_MI(x, y, numBinx, numBiny) rxy = pearson_corr(x, y) exy = entropyx_y(x, y, numBinx, numBiny) eyx = entropyx_y(y, x, numBinx, numBiny) MIE = MI_Entropy(x, y, numBinx, numBiny) Ixy = propuesta_Ixy(x, y, numBinx, numBiny) Iyx = propuesta_Iyx(x, y, numBinx, numBiny) Ixy2 = propuesta2_Ixy(x, y, numBinx, numBiny) Iyx2 = propuesta2_Iyx(x, y, numBinx, numBiny) PMD = propuesta_mutual_dependency(x, y, numBinx, numBiny) PMD2 = propuesta2_mutual_dependency(x, y, numBinx, numBiny) # d_cor = d_corr(x,y) # Mic = MIC(x,y) mitexto.set( 'Entropy of x: ' + str(ex) + '\n' + 'Entropy of y: ' + str(ey) + '\n' + 'Mutual information: ' + str(MI) + '\n' + 'Mutual info with entropy: ' + str(MIE) + '\n' + '\n' + '(Max=1)Normalized Mutual info: ' + str(R) + '\n' + '(Max=1)Pearson Correlation: ' + str(rxy) + '\n' + # '(Max=1)Distance Correlation: ' + str(d_cor) + '\n' + # '(Max=1)MIC: ' + str(Mic) + '\n' + '(Max=1)Mutual Dependency: ' + str(PMD) + '\n' + '(Max=1)Mutual Dependency2: ' + str(PMD2) + '\n' + '\n' + 'Entropy of X|Y = ' + str(exy) + '\n' + 'Entropy of Y|X = ' + str(eyx) + '\n' + '(Max=1)Information in Y of X: ' + str(Ixy) + '\n' + '(Max=1)Information in X of Y: ' + str(Iyx) + '\n' + '(Max=1)Information2 in Y of X: ' + str(Ixy2) + '\n' + '(Max=1)Information2 in X of Y: ' + str(Iyx2)) if plot_sample.get(): sam.plot_sample(samples, numBinx, numBiny, step.get()) if plot_compare.get(): obj.plot_compare2(numBinx, numBiny, noise.get(), step.get()) if plot_propuse.get(): sam.plot_propose(samples, numBinx) if plot_partition.get(): sam.plot_partition(samples, numBinx, numBiny) if plot_converge_in_samples.get(): Converge_in_samples(numBinx, numBiny, obj) if samples_check.get(): samples_grid(numBinx, numBiny)
def save_wav(self) -> None: # Save waveform duration, ok = QtGui.QInputDialog.getInt(self, "Seconds of Audio:", "Seconds:", 1, 0, MAX_VAL, STEP_VAL) if ok: dialog = QtGui.QFileDialog() path = dialog.getSaveFileName(self, 'Save File', os.getenv('HOME'), 'WAV (*.wav)') if path[0] != '': samples = Samples() expression = copy.deepcopy(self.expression) samples.set_expression(expression) samples.gen_write_16(path[0], duration)
def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.setup_layout() self.setup_waveform() self.setup_instructions() self.layout.nextRow() self.setup_spectrograph() self.layout.nextRow() self.setup_editor() self.setup_index() self.samples = Samples(self.waveform.data_available, self.spectrograph.data_available) self.copy_func_to_samples() self.copy_func_to_editor_and_display() self.setup_pyaudio() self.stream.start_stream()
def __init__(self, json_path, out_dir): # Attributes # self.out_dir = out_dir self.json_path = json_path # Parse # with open(json_path) as handle: self.info = json.load(handle) # Basic # self.account = self.info['uppmax_id'] self.run_num = self.info['run_num'] self.run_label = self.info['run_id'] self.project_short_name = self.info['project'] self.project_long_name = self.info['project_name'] # Own attributes # self.num = self.info['pool_num'] self.label = self.info['pool_id'] self.short_name = self.info['pool'] self.long_name = self.info['pool_name'] self.id_name = "run%03d-pool%02d" % (self.run_num, self.num) # Special # self.samples = Samples(self) self.primers = TwoPrimers(self) # Second init # self.loaded = False
def command_parser(): ''' THIS PART CAN PARSER THE COMMAND PARAMETERS. Value 0 is off, 1 is open. ''' try: source_path = "" save_path = "" # The default value of data_deal model. iamges_deal = 0 size_x = 28 size_y = 28 channel = 3 # samples model parameters samples = 0 no_replace = 0 replace = 0 number = 10000 # train and test the model train_test = 0 dataset = 'mnist.pkl.gz' config_path = "../config-example" n_y = 10 m1 = 0 m2 = 0 opts, args = getopt.getopt(sys.argv[1:], "ho:", \ ["help", \ "images_deal", \ "images_path=", \ "size_x=", \ "size_y=" , \ "channel=", \ "samples", \ "no_replace=", \ "replace=", \ "source_path=" , \ "number=", \ "save_path=", \ "train_test", \ "dataset=", \ "config_path=", \ "n_y=" , \ "m1=", \ "m2=" ]) #print opts,args for x, y in opts: if x in ("-h", "--help"): print "-----------------------------------------------------------" print "ensemb: command line brew" print "usage : python ensemb.py <command> <args>" print "---------------------------------------------------------------------" print "images_deal : deal the image to pickle file" print "---------- ensemb.py --images_deal " print "---------- --source_path=/home/gd/ensemble-nn/data/image" print "---------- --size_x=28" print "---------- --size_y=28 " print "---------- --channel=3" print "---------------------------------------------------------------------" print "samples : sample the samples from the datasets" print "---------- ensemb.py --samples " print "---------- --no_replace=1" print "---------- --replace=0 " print "---------- --source_path=./data/mnist.pkl.gz" print "---------- --number=30000" print "---------- --save_path=./data/mnist-sample-replace.pkl" print "---------------------------------------------------------------------" print "train_test : train and test the model" print "---------- ensemb.py --train_test " print "---------- --dataset =/home/gd/ensemble-nn/data/mnist.pkl.gz" print "---------- --config_path=./config-example " print "---------- --n_y =10" print "---------- --m1 = 1" print "---------------------------------------------------------------------" # MODEL IMAGES_DEAL if x in ("--images_deal"): print "Deal the iamge .." iamges_deal = 1 if x in ("--source_path"): images_path = y if x in ("--size_x"): size_x = int(y) if x in ("--size_y"): size_y = int(y) if x in ("--channel"): channel = int(y) # MODEL SAMPLES if x in ("--samples"): print "sampling .." samples = 1 if x in ("--no_replace"): no_replace = int(y) if x in ("--replace"): replace = int(y) if x in ("--source_path"): source_path = y if x in ("--number"): number = int(y) if x in ("--save_path"): save_path = y # TRAIN AND TEST A MODEL if x in ("--train_test"): print "train and test .." train_test = 1 if x in ("--dataset"): dataset = y if x in ("--config_path"): config_path = y if x in ("--n_y"): n_y = int(y) if x in ("--m1"): m1 = int(y) if x in ("--m2"): m2 = int(y) if iamges_deal == 1: source_path = "/home/gd/ensemble-nn/data/image" DealDate_deal(image_path=source_path, size_x=size_x, size_y=size_y, channel=channel) if samples == 1: source_path = "/home/gd/ensemble-nn/data/mnist.pkl.gz" save_path = "/home/gd/ensemble-nn/data/mnist-sample-replace.pkl" if no_replace == 0 and replace == 0: print "no_replace or replace ?" if no_replace == 1 and replace == 1: print "no_replace or replace ?" if no_replace == 1: instance = Samples() instance.no_replace_sample(source_path, number, save_path) if replace == 1: instance = Samples.Samples() instance.replace_sample(source_path, number, save_path) if train_test == 1: if m1 == 0 and m2 == 0: print "adaboost-m1 or adaboost-m2 ?" if m1 == 1 and m2 == 1: print "adaboost-m1 or adaboost-m2 ?" if m1 == 1 and m2 == 0: instance = Controller() instance.m1_controller(dirctory=config_path, dataset=dataset, n_y=n_y) if m1 == 0 and m2 == 1: instance = Controller() instance.m2_controller(dirctory=config_path, dataset=dataset, n_y=n_y) sys.exit() except Exception as err: print err
def plot_select(): ind = list1.curselection() if list1.curselection() != (): if ind[0] == 0: sam = Samples(tipo=0) samples = sam.get_sin(numberSam.get(), noise.get()) case_function(samples, sam) elif ind[0] == 1: sam = Samples(tipo=1) samples = sam.get_square(numberSam.get(), noise.get()) case_function(samples, sam) elif ind[0] == 2: sam = Samples(tipo=2) samples = sam.get_blur(numberSam.get(), noise.get()) case_function(samples, sam) elif ind[0] == 3: sam = Samples(tipo=3) samples = sam.get_cuadratic(numberSam.get(), noise.get()) case_function(samples, sam) elif ind[0] == 4: sam = Samples(tipo=4) samples = sam.get_diagonal_line(numberSam.get(), noise.get()) case_function(samples, sam) elif ind[0] == 5: sam = Samples(tipo=5) samples = sam.get_horizontal_line(numberSam.get(), noise.get()) case_function(samples, sam) elif ind[0] == 6: sam = Samples(tipo=6) samples = sam.get_vertical_line(numberSam.get(), noise.get()) case_function(samples, sam) elif ind[0] == 7: sam = Samples(tipo=7) samples = sam.get_x(numberSam.get(), noise.get()) case_function(samples, sam) elif ind[0] == 8: sam = Samples(tipo=8) samples = sam.get_circle(numberSam.get(), noise.get()) case_function(samples, sam) elif ind[0] == 9: sam = Samples(tipo=9) samples = sam.get_curve_x(numberSam.get(), noise.get()) case_function(samples, sam) elif ind[0] == 10: sam = Samples(tipo=10) samples = sam.get_diagonal_line2(numberSam.get(), noise.get()) case_function(samples, sam) elif ind[0] == 11: sam = Samples(tipo=11) samples = sam.get_dependent(numberSam.get()) case_function(samples, sam) elif ind[0] == 12: sam = Samples(tipo=12) samples = sam.get_independent(numberSam.get()) case_function(samples, sam) elif ind[0] == 13: sam = Samples(tipo=13) samples = sam.get_corr(numberSam.get(), noise.get()) case_function(samples, sam) elif ind[0] == 14: sam = Samples(tipo=14) samples = sam.get_file() case_function(samples, sam) else: mitexto.set('Error') else: mitexto.set('Select a type')
DIR: 'data/facedata', HEIGHT: 68, WIDTH: 61, LABEL: 2, PIXELS: None }, DIGIT: { DIR: 'data/digitdata', HEIGHT: 20, WIDTH: 29, LABEL: 10, PIXELS: None } } samples = Samples(map.get(inp).get(DIR)) dataClassifier = DataClassifier( map.get(inp).get(HEIGHT), map.get(inp).get(WIDTH), map.get(inp).get(LABEL), map.get(inp).get(PIXELS)) perceptronClassifier = PerceptronClassifier(dataClassifier.FEATURES, dataClassifier.LABELS) samples.readFiles() dataset = 0 featureValueListForAllTrainingImages, actualLabelForTrainingList = dataClassifier.extractFeatures( samples.train_lines_itr, samples.train_labelsLines_itr) TOTALDATASET = len(featureValueListForAllTrainingImages)
HEIGHT: 68, WIDTH: 61, LABEL: 2, PIXELS: None }, DIGIT: { DIR: 'data/digitdata', HEIGHT: 20, WIDTH: 29, LABEL: 10, PIXELS: None } } dataType = map.get(inp) samples = Samples(dataType.get(DIR)) dataClassifier = DataClassifier(dataType.get(HEIGHT), dataType.get(WIDTH), dataType.get(LABEL), dataType.get(PIXELS), gridSize) samples.readFiles() ''' Extracting Features from the Training Data ''' featureValueListForAllTrainingImages, actualLabelForTrainingList = \ dataClassifier.extractFeatures(samples.train_lines_itr, samples.train_labelsLines_itr) runErrorList = [] runTimeList = [] runDataSetIncrements = [] for _ in range(0, 3):