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39 def _load_text(text_path): 40 with open(text_path + '.txt', 'r', encoding='utf-8') as fin: 41 text = ' '.join([line.strip() for line in fin.readlines()]) 42 return text
29 def read_text_file(filename, encoding="utf-8"): 30 """ 31 Reads a file under python3 with encoding (default UTF-8). 32 Also works under python2, without encoding. 33 Uses the EAFP (https://docs.python.org/2/glossary.html#term-eafp) 34 principle. 35 """ 36 try: 37 with open(filename, 'r', encoding) as f: 38 r = f.read() 39 except TypeError: 40 with open(filename, 'r') as f: 41 r = f.read() 42 return r
28 def read_text(filename, **kwargs): 29 """read_text""" 30 memory = kwargs.get("memory", "") 31 offset = kwargs.get("offset", 0) 32 length = kwargs.get("length", -1) 33 return core_ops.io_read_text( 34 filename, offset=offset, length=length, memory=memory)
12 def readFile(): 13 #make the format of the csv file. Our format is a vector with 13 features and a label which show the condition of the 14 #sample hc/pc : helathy case, parkinson case 15 names = ['Feature1', 'Feature2', 'Feature3', 'Feature4','Feature5','Feature6','Feature7','Feature8','Feature9', 16 'Feature10','Feature11','Feature12','Feature13','Label'] 17 18 #path to read the samples, samples consist from healthy subjects and subject suffering from Parkinson's desease. 19 path = '' 20 #read file in csv format 21 data = pd.read_csv(path,names=names ) 22 23 #return an array of the shape (2103, 14), lines are the samples and columns are the features as we mentioned before 24 return data
20 def readFile(): 21 #make the format of the csv file. Our format is a vector with 13 features and a label which show the condition of the 22 #sample hc/pc : helathy case, parkinson case 23 names = ['Feature1', 'Feature2', 'Feature3', 'Feature4','Feature5','Feature6','Feature7','Feature8','Feature9', 24 'Feature10','Feature11','Feature12','Feature13','Label'] 25 26 #path to read the samples, samples consist from healthy subjects and subject suffering from Parkinson's desease. 27 path = 'mfcc_multiclass.txt' 28 #read file in csv format 29 data = pd.read_csv(path,names=names ) 30 31 #return an array of the shape (2103, 14), lines are the samples and columns are the features as we mentioned before 32 return data
141 def readFile(pFile): 142 return pd.read_csv(pFile, sep='\t', header=None)
10 def __read(filename): 11 field_names = ['date', 'value', 'metaID'] 12 entry_format = '
37 def readcsv(filename, header=True): 38 return pd.read_csv(filename, header=None) if not header else pd.read_csv(filename)
1 def read_text_src(text_src, delimiter): 2 if isinstance(text_src, str): 3 with open(text_src, 'r') as f: 4 text_src = [line.split(delimiter) for line in f] 5 elif not isinstance(text_src, list): 6 raise TypeError('text_src should be list or str') 7 return text_src
17 def raw_reader(path): 18 with open(path, 'rb') as f: 19 bin_data = f.read() 20 return bin_data