SYMBOL INDEX (227 symbols across 52 files)

FILE: Computer Vision/Real Time Sign Language Interpretation App/ReactComputerVisionTemplate/src/App.js
  function App (line 11) | function App() {

FILE: Deep Learning/Classification/Melenoma_Classification/deep-learning-models/CNN_model.py
  function simple_CNN (line 13) | def simple_CNN(train_data_shape,n_classes):
  function MobileNet (line 39) | def MobileNet(num_classes, is_trainable ):
  function VGG_16 (line 71) | def VGG_16(num_classes,is_trainable):
  function Inception_v3 (line 101) | def Inception_v3(num_classes,is_trainable):
  function InceptionResNetV2 (line 129) | def InceptionResNetV2(num_classes,is_trainable):

FILE: Deep Learning/Classification/Melenoma_Classification/deep-learning-models/main.py
  function select_CNN_model (line 16) | def select_CNN_model(model_name,num_classes,trainable,input_shape):
  function getLayerIndexByName (line 39) | def getLayerIndexByName(model, layername):

FILE: Deep Learning/Classification/Melenoma_Classification/deep-learning-models/training.py
  function training_model (line 19) | def training_model(model,train_data,train_labels,val_data,val_labels,tes...
  function plotting_train_val_metrics (line 46) | def plotting_train_val_metrics(history,evaulation_metric):

FILE: Deep Learning/Classification/Melenoma_Classification/evaluation-metrics/classification_metrics.py
  function confusion_matrix_calc (line 11) | def confusion_matrix_calc(predicted_labels,true_labels,num_classes,title):

FILE: Deep Learning/Classification/Melenoma_Classification/evaluation-metrics/f1_score.py
  function f1 (line 10) | def f1(y_true, y_pred):
  function f1_loss (line 24) | def f1_loss(y_true, y_pred):
  function f1_micro (line 38) | def f1_micro(y_true, y_pred):

FILE: Deep Learning/Classification/Melenoma_Classification/loading and storing/loading_images.py
  function load_images_from_folder (line 11) | def load_images_from_folder(folder,width,height):

FILE: Deep Learning/Classification/Melenoma_Classification/loading and storing/loading_storing_h5py.py
  function storing_h5py (line 12) | def storing_h5py(input_data,hdf5_dir):
  function read_h5py (line 20) | def read_h5py(hdf5_dir,num_images):

FILE: Deep Learning/Classification/Melenoma_Classification/preprocessing/exploration.py
  function bar_plot (line 10) | def bar_plot(input_data,title):
  function class_counts_proportions (line 20) | def class_counts_proportions(labels):

FILE: Deep Learning/Classification/Melenoma_Classification/preprocessing/preprocessing.py
  function splitting_normalization (line 19) | def splitting_normalization(input_data, labels):
  function splitting_classes (line 43) | def splitting_classes(input_data,input_labels):
  function resizing_data (line 78) | def resizing_data(input_data,width, height):
  function resampling (line 101) | def resampling(train_data,train_labels,resampling_type,resampling_stragey):

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/Best_mask.py
  function main (line 10) | def main():

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/Best_mask2.py
  function main (line 10) | def main():

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/Model.py
  function inner_loop (line 17) | def inner_loop(data,labels,number_of_cv,feature_selection_type,Hyperpara...
  function model (line 52) | def model(data_train,labels_train,data_validation,labels_validation,mask...
  function create_mask (line 92) | def create_mask(data,labels,number_of_cv,feature_selection_type,Hyperpar...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/confidence_interval_mask.py
  function main (line 9) | def main():

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/data_preprocessing.py
  function standarization (line 29) | def standarization(data):
  function quantile_transform (line 37) | def quantile_transform(data, random_state):
  function gussian_filter (line 43) | def gussian_filter(data, sigma):
  function signal_clean (line 50) | def signal_clean(data):
  function robust_scaler (line 56) | def robust_scaler(data):
  function MinMax_scaler (line 63) | def MinMax_scaler(data):
  function dublicate (line 70) | def dublicate(data, number):
  function concat (line 78) | def concat(data1, data2):
  function shuffling (line 84) | def shuffling(data, labels):
  function PowerTransform (line 91) | def PowerTransform(data):
  function coefficient_of_variance (line 98) | def coefficient_of_variance(data):
  function density_ratio_estimation (line 105) | def density_ratio_estimation(train_data, test_data):
  function outliers (line 112) | def outliers(train_data, train_labels, number_of_neighbours):
  function novelty (line 122) | def novelty(train_data, train_labels, test_data, test_labels, number_of_...
  function upsampling (line 134) | def upsampling(data,labels):
  function resampling (line 165) | def resampling(data, labels):
  function synthetic (line 182) | def synthetic(data, labels, num):
  function KSTest (line 189) | def KSTest(train_data, test_data, step):
  function removeDuplicates (line 208) | def removeDuplicates(listofElements):
  function ica (line 222) | def ica(data, number_of_combonents):
  function pca (line 230) | def pca(data, number_of_combonents):
  function g_po_sk (line 240) | def g_po_sk(input=None):
  function sp (line 255) | def sp(input):
  function po (line 268) | def po(input):
  function g_sp (line 279) | def g_sp(input):
  function g_po (line 292) | def g_po(input):
  function g_sk (line 305) | def g_sk(input):
  function sp_sk (line 318) | def sp_sk(input):
  function sp_po (line 330) | def sp_po(input):
  function po_sk (line 342) | def po_sk(input):
  function noise_all (line 354) | def noise_all(input):
  function apply_noise_manytypes (line 369) | def apply_noise_manytypes(data):
  function g_f (line 383) | def g_f(input):
  function m_f (line 395) | def m_f(input):
  function us_f (line 407) | def us_f(input):  # unsharp filter
  function c_f (line 419) | def c_f(input):
  function w_f (line 431) | def w_f(input):
  function g_m_f (line 443) | def g_m_f(input):
  function g_us_f (line 456) | def g_us_f(input):
  function m_us_f (line 469) | def m_us_f(input):
  function g_c_f (line 482) | def g_c_f(input):
  function g_w_f (line 495) | def g_w_f(input):
  function c_w_f (line 508) | def c_w_f(input):
  function m_w_f (line 521) | def m_w_f(input):
  function m_c_f (line 534) | def m_c_f(input):
  function m_g_c_f (line 547) | def m_g_c_f(input):
  function m_g_us_f (line 561) | def m_g_us_f(input):
  function c_w_us_f (line 575) | def c_w_us_f(input):
  function c_w_us_g_f (line 589) | def c_w_us_g_f(input):
  function c_w_us_m_f (line 604) | def c_w_us_m_f(input):
  function all_f (line 619) | def all_f(input):
  function apply_filter_manytypes (line 635) | def apply_filter_manytypes(data):
  function concatination (line 659) | def concatination(data1, data2):
  function flatten (line 668) | def flatten(data):
  function deflatten (line 673) | def deflatten(data, shape):
  function select_max_features (line 678) | def select_max_features(mask, number_of_featrues):
  function transposnig (line 685) | def transposnig(input_data, order):
  function size_editing (line 689) | def size_editing(data, final_height):
  function depth_reshapeing (line 715) | def depth_reshapeing(data):
  function converting_nii_to_npz (line 737) | def converting_nii_to_npz(file_name):
  function labels_convert_one_hot (line 743) | def labels_convert_one_hot(labels):

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/AlexNet.py
  function model (line 17) | def model(train_data_whole,train_labels_whole,test_data,test_labels,opt,...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/CNN.py
  function CNN_main (line 34) | def CNN_main(train_data,test_data,result_path,train_labels,test_labels,n...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/CNN_feature_extractor.py
  function take (line 38) | def take(n, iterable):
  function fully_connected_layer (line 42) | def fully_connected_layer(dropouts,activation_function):
  function fully_connected_layer_fit (line 65) | def fully_connected_layer_fit(features_train,features_test,train_labels,...
  function features_pretrained_model (line 99) | def features_pretrained_model(model,train_data,test_data):
  function features_saved_model (line 109) | def features_saved_model(train_data,test_data,model,intermediate_layer,r...
  function classifer_fit_testing (line 134) | def classifer_fit_testing(features_train,train_labels,features_test,test...
  function CNN_feature_extraction_classsification (line 190) | def CNN_feature_extraction_classsification(feature_extractor_parameters,...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/DenseNet121.py
  function model (line 16) | def model(train_data_whole,train_labels_whole,test_data,test_labels,opt,...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/InceptionResNetV2.py
  function model (line 17) | def model (train_data_whole,train_labels_whole,test_data,test_labels,opt...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/LeNet.py
  function model (line 17) | def model (train_data_whole,train_labels_whole,test_data,test_labels,opt...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/ResNet50.py
  function model (line 15) | def model(train_data_whole,train_labels_whole,test_data,test_labels,opt,...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/VGG.py
  function model (line 15) | def model(train_data_whole,train_labels_whole,test_data,test_labels,opt,...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/VGG_pretrained.py
  function model (line 18) | def model (train_data_whole,train_labels_whole,test_data,test_labels,opt...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/ZFNet.py
  function model (line 17) | def model(train_data_whole,train_labels_whole,test_data,test_labels,opt,...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/optimizers.py
  function choosing (line 6) | def choosing(optimizer):

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/simple_model.py
  function model (line 18) | def model (train_data_whole,train_labels_whole,test_data,test_labels,opt...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/evaluation/metrics.py
  function auc_roc (line 1) | def auc_roc(y_true, y_pred):

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/evaluation/model_evaluation.py
  function testing_and_printing (line 11) | def testing_and_printing(classification_model,classification_train,best_...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/main.py
  function main (line 11) | def main():

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/preprocessing/data_augmentation.py
  function load_obj (line 33) | def load_obj(obj):
  function flipping (line 43) | def flipping(img,axis):
  function flipping_HV (line 49) | def flipping_HV(img):
  function rotate (line 53) | def rotate(img,angle):
  function shifting (line 58) | def shifting(img,shift_amount):
  function zooming (line 64) | def zooming(img,zooming_amount):
  function add_gaussian_noise (line 69) | def add_gaussian_noise(X_imgs):
  function transposnig (line 87) | def transposnig(input_data,order):
  function mask_print (line 92) | def mask_print(input,mask,name):
  function slicing (line 110) | def slicing(len1,len2 ):

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/preprocessing/data_preprocessing.py
  function Normalization (line 25) | def Normalization(data):
  function standarization (line 29) | def standarization(data):
  function quantile_transform (line 35) | def quantile_transform(data,random_state):
  function gussian_filter (line 40) | def gussian_filter(data,sigma):
  function signal_clean (line 46) | def signal_clean(data):
  function robust_scaler (line 50) | def robust_scaler(data):
  function MinMax_scaler (line 56) | def MinMax_scaler(data):
  function dublicate (line 62) | def dublicate(data,number):
  function concat (line 68) | def concat(data1,data2):
  function shuffling (line 74) | def shuffling(data,labels):
  function PowerTransform (line 80) | def PowerTransform(data):
  function coefficient_of_variance (line 86) | def coefficient_of_variance(data):
  function density_ratio_estimation (line 92) | def density_ratio_estimation(train_data,test_data):
  function outliers (line 98) | def outliers(train_data,train_labels,number_of_neighbours):
  function novelty (line 107) | def novelty(train_data,train_labels,test_data,test_labels,number_of_neig...
  function upsampling (line 117) | def upsampling(data,labels):
  function synthetic (line 134) | def synthetic(data,labels,num):
  function KSTest (line 140) | def KSTest(train_data,test_data,step):
  function removeDuplicates (line 162) | def removeDuplicates(listofElements):
  function transposnig (line 176) | def transposnig(input_data, order):
  function size_editing (line 180) | def size_editing(data, final_height):
  function depth_reshapeing (line 202) | def depth_reshapeing(data):
  function converting_nii_to_npz (line 224) | def converting_nii_to_npz(file_path):
  function labels_convert_one_hot (line 230) | def labels_convert_one_hot(labels):
  function data_resampling (line 241) | def data_resampling(data,factors):

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/preprocessing/preprocessing_methods.py
  function preprocessing (line 5) | def preprocessing(train_data,test_data,train_labels,test_labels,method,s...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/storing_loading/generate_result_.py
  function out_result (line 16) | def out_result(test_data,test_labels,original_mask,created_mask,model):
  function print_result_2models (line 30) | def print_result_2models(test_data,test_labels,original_mask,model,model...
  function print_result (line 89) | def print_result(test_data,test_labels,original_mask,created_mask,model,...
  function mask_print (line 143) | def mask_print(original_mask,created_mask,output_dir,name):
  function weights_print (line 155) | def weights_print(original_mask, weights, output_dir,name):
  function cnn_save_result (line 168) | def cnn_save_result(test_accuracy,model,model_name,result_path):
  function create_results_dir (line 182) | def create_results_dir(results_directory):

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/storing_loading/load_data.py
  function find_path (line 10) | def find_path(file_name):
  function train_data_3d (line 32) | def train_data_3d(train_Con_file_name, train_AD_file_name):
  function test_data_3d (line 43) | def test_data_3d(test_Con_file_name,test_AD_file_name):
  function mask (line 55) | def mask(mask_name):

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/generate_result.py
  function out_result (line 16) | def out_result(test_data,test_labels,original_mask,created_mask,model):
  function print_result_2models (line 30) | def print_result_2models(test_data,test_labels,original_mask,model,model...
  function print_result (line 89) | def print_result(test_data,test_labels,original_mask,created_mask,model,...
  function mask_print (line 143) | def mask_print(original_mask,created_mask,output_dir,name):
  function weights_print (line 155) | def weights_print(original_mask, weights, output_dir,name):
  function cnn_save_result (line 168) | def cnn_save_result(test_accuracy,model,model_name,result_path):
  function create_results_dir (line 183) | def create_results_dir(results_directory):

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/hyper_opt.py
  function hyperparameter_selection (line 17) | def hyperparameter_selection(data,labels,number_of_cv,feature_selection_...
  function model (line 83) | def model(data_train,labels_train,mask,data_validation=None,labels_valid...
  function create_mask (line 126) | def create_mask(data,labels,number_of_cv,feature_selection_type,Hyperpar...
  function model_1D (line 158) | def model_1D(data_train,labels_train,mask,data_validation=None,labels_va...
  function model_1D_calibrate (line 200) | def model_1D_calibrate(data_train,labels_train,mask,data_validation=None...
  function model_reduced (line 244) | def model_reduced(data_train,labels_train,mask,data_validation=None,labe...

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/load_data.py
  function find_path (line 10) | def find_path(file_name):
  function train_data_3d (line 32) | def train_data_3d(train_Con_file_name, train_AD_file_name):
  function test_data_3d (line 43) | def test_data_3d(test_Con_file_name,test_AD_file_name):
  function mask (line 55) | def mask(mask_name):

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/main.py
  function main (line 10) | def main():

FILE: Machine Learning/Classification/Alzhimers CV-BOLD Classification/pykliep.py
  class DensityRatioEstimator (line 4) | class DensityRatioEstimator:
    method __init__ (line 28) | def __init__(self, max_iter=5000, num_params=[.1,.2], epsilon=1e-4, cv...
    method fit (line 48) | def fit(self, X_train, X_test, alpha_0=None):
    method _fit (line 100) | def _fit(self, X_train, X_test, num_parameters, sigma, alpha_0=None):
    method _calculate_j (line 127) | def _calculate_j(self, X_test, sigma):
    method score (line 130) | def score(self, X_test):
    method _reshape_X (line 135) | def _reshape_X(X):
    method _select_param_vectors (line 141) | def _select_param_vectors(self, X_test, sigma, num_parameters):
    method _phi (line 147) | def _phi(self, X, sigma=None):
    method _find_alpha (line 156) | def _find_alpha(self, alpha_0, X_train, X_test, num_parameters, sigma,...
    method predict (line 174) | def predict(self, X, sigma=None):

FILE: Natural_Language_processing/Sentiment-analysis/sevre/model.py
  class LSTMClassifier (line 3) | class LSTMClassifier(nn.Module):
    method __init__ (line 8) | def __init__(self, embedding_dim, hidden_dim, vocab_size):
    method forward (line 21) | def forward(self, x):

FILE: Natural_Language_processing/Sentiment-analysis/sevre/predict.py
  function model_fn (line 18) | def model_fn(model_dir):
  function input_fn (line 49) | def input_fn(serialized_input_data, content_type):
  function output_fn (line 56) | def output_fn(prediction_output, accept):
  function predict_fn (line 60) | def predict_fn(input_data, model):

FILE: Natural_Language_processing/Sentiment-analysis/sevre/utils.py
  function review_to_words (line 13) | def review_to_words(review):
  function convert_and_pad (line 25) | def convert_and_pad(word_dict, sentence, pad=500):

FILE: Natural_Language_processing/Sentiment-analysis/train/model.py
  class LSTMClassifier (line 3) | class LSTMClassifier(nn.Module):
    method __init__ (line 8) | def __init__(self, embedding_dim, hidden_dim, vocab_size):
    method forward (line 21) | def forward(self, x):

FILE: Natural_Language_processing/Sentiment-analysis/train/train.py
  function model_fn (line 14) | def model_fn(model_dir):
  function _get_train_data_loader (line 45) | def _get_train_data_loader(batch_size, training_dir):
  function train (line 58) | def train(model, train_loader, epochs, optimizer, loss_fn, device):

FILE: Natural_Language_processing/plagiarism-detector-web-app/helpers.py
  function create_datatype (line 9) | def create_datatype(df, train_value, test_value, datatype_var, compare_d...
  function train_test_dataframe (line 47) | def train_test_dataframe(clean_df, random_seed=100):
  function process_file (line 70) | def process_file(file):
  function create_text_column (line 85) | def create_text_column(df, file_directory='data/'):

FILE: Natural_Language_processing/plagiarism-detector-web-app/problem_unittests.py
  class AssertTest (line 10) | class AssertTest(object):
    method __init__ (line 12) | def __init__(self, params):
    method test (line 15) | def test(self, assert_condition, assert_message):
  function _print_success_message (line 18) | def _print_success_message():
  function test_numerical_df (line 22) | def test_numerical_df(numerical_dataframe):
  function test_containment (line 48) | def test_containment(complete_df, containment_fn):
  function test_lcs (line 81) | def test_lcs(df, lcs_word):
  function test_data_split (line 126) | def test_data_split(train_x, train_y, test_x, test_y):

FILE: Natural_Language_processing/plagiarism-detector-web-app/source_pytorch/model.py
  class BinaryClassifier (line 7) | class BinaryClassifier(nn.Module):
    method __init__ (line 19) | def __init__(self, input_features, hidden_dim, output_dim):
    method forward (line 34) | def forward(self, x):

FILE: Natural_Language_processing/plagiarism-detector-web-app/source_pytorch/predict.py
  function model_fn (line 15) | def model_fn(model_dir):
  function input_fn (line 44) | def input_fn(serialized_input_data, content_type):
  function output_fn (line 52) | def output_fn(prediction_output, accept):
  function predict_fn (line 62) | def predict_fn(input_data, model):

FILE: Natural_Language_processing/plagiarism-detector-web-app/source_pytorch/train.py
  function model_fn (line 12) | def model_fn(model_dir):
  function _get_train_data_loader (line 40) | def _get_train_data_loader(batch_size, training_dir):
  function train (line 54) | def train(model, train_loader, epochs, criterion, optimizer, device):

FILE: Natural_Language_processing/plagiarism-detector-web-app/source_sklearn/train.py
  function model_fn (line 17) | def model_fn(model_dir):

FILE: time-series-analysis/Power-consumption-forecasting/txt_preprocessing.py
  function create_df (line 13) | def create_df(text_file, sep=';', na_values=['nan','?']):
  function fill_nan_with_mean (line 40) | def fill_nan_with_mean(df):