목록공부/Python (70)

잠토의 잠망경

[python] glob, Directory 하위 파일 List 읽기

지정된 위치의 File List를 읽기위한 방법 Github https://github.com/yiwonjae/Project_Python_Lib/blob/master/Files/Directorys.py import glob ''' 목적: 원하는 폴더에 있는 file 이름을 찾아서 return한다. i ) path : 폴더 및 파일 이름, *.txt, *.* 사용 가능 o ) list : file 이름 list ''' def findFiles(path:str)->list: fileList = glob.glob(path) print(fileList) return fileList if __name__ == '__main__': path = "../datas/sincontan*.txt" files = findF..
공부/Python 2020. 3. 22. 09:55
[python] d3.js 활용 sample

http://jakevdp.github.io/blog/2013/12/19/a-d3-viewer-for-matplotlib/ A D3 Viewer for Matplotlib Visualizations | Pythonic Perambulations Conclusion¶ As I mentioned above, there is still a lot to be done to make this a truly useful tool for data exploration: right now, the list of usupported features alone likely dwarfs the size of the current code base! But as a proof-of-concept, I think th jake..
공부/Python 2020. 2. 21. 09:33
[ML] LSTM - Univariate Bidirectional LSTM Models

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p130.py 0. 목표 ''' data [10, 20, 30, 40, 50, 60, 70, 80, 90] X(input), y(output) 10, 20, 30, 40 20, 30, 40, 50 30, 40, 50, 60 ''' 1. DATA raw_seq = np.asarray([10, 20, 30, 40, 50, 60, 70, 80, 90]) 2. DATA 정제 X import numpy as np from numpy import ndarray def split_sequence(sequence:ndarray, n_steps:int)->(ndarray, ndarray): x =..
공부/Python 2020. 2. 16. 17:42
[ML] LSTM - Univariate stack LSTM Models

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p128.py 0. 목표 ''' data [10, 20, 30, 40, 50, 60, 70, 80, 90] X(input), y(output) 10, 20, 30, 40 20, 30, 40, 50 30, 40, 50, 60 ''' 1. DATA raw_seq = np.asarray([10, 20, 30, 40, 50, 60, 70, 80, 90]) 2. DATA 정제 X import numpy as np from numpy import ndarray def split_sequence(sequence:ndarray, n_steps:int)->(ndarray, ndarray): x =..
공부/Python 2020. 2. 16. 17:37
[ML] LSTM - Univariate LSTM Models

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p127_LSTM.py 0. 목표 ''' data [10, 20, 30, 40, 50, 60, 70, 80, 90] X(input), y(output) 10, 20, 30, 40 20, 30, 40, 50 30, 40, 50, 60 ''' 1. DATA raw_seq = np.asarray([10, 20, 30, 40, 50, 60, 70, 80, 90]) 2. DATA 정제 X import numpy as np from numpy import ndarray def split_sequence(sequence:ndarray, n_steps:int)->(ndarray, ndarray)..
공부/Python 2020. 2. 16. 17:32
[ML] CNN - Multiple Parallel Input and Multi-step Output

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p119.py 0. 목표 [ 10 15 25] [ 20 25 45] [ 30 35 65] [ 40 45 85] [ 50 55 105] [ 60 65 125] [ 70 75 145] [ 80 85 165] [ 90 95 185] input [ 10 15 25] [ 20 25 45] [ 30 35 65] output [ 40 45 85] [ 50 55 105] 1. DATA in_seq1 = np.asarray([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = np.asarray([15, 25, 35, 45, 55, 65, 75, 85, 95]) o..
공부/Python 2020. 2. 16. 16:47
[ML] Multiple Input Multi-step Output

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p113.py 0. 목표 input 多, output 多step 1. DATA in_seq1 = np.asarray([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = np.asarray([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = np.asarray([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) in_seq1:ndarray = in_seq1.reshape((len(in_seq1), 1)) in_seq2:ndarray = in_seq2.reshape((len..
공부/Python 2020. 2. 16. 10:12
[ML] Multi-step CNN Models

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p111.py 0. 목표 output이 여러단계를 원할 때 1. DATA raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] 2. DATA 정제 X from numpy import ndarray import numpy as np def split_sequence(sequence:list, n_steps_in:int, n_steps_out:int)->(ndarray, ndarray): x = [] y = [] for i in range(len(sequence)): if(i+n_steps_in+n_steps_out > len(sequence)): bre..
공부/Python 2020. 2. 15. 21:46
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