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Kaggle-Nomad2018-master

于 2020-03-11 发布
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代码说明：

说明：  毕业设计之跳频通信系统的设计与仿真。努力学习noma技术，顺利毕业。(Frequency hopping communication system)

文件列表：

Kaggle-Nomad2018-master, 0 , 2018-03-12
Kaggle-Nomad2018-master\.gitattributes, 66 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\504notebook-checkpoint.ipynb, 15146 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\DMLP-checkpoint.ipynb, 22418 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\catmodel-checkpoint.ipynb, 7097 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\cvmodel_set-checkpoint.ipynb, 4840 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\dnn_keras-checkpoint.ipynb, 11284 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\dnn_keras_f-checkpoint.ipynb, 389362 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\feature_engineer-checkpoint.ipynb, 12281 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\k_neural_feat-checkpoint.ipynb, 6241 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\lightmodel-checkpoint.ipynb, 14382 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\mean_script-checkpoint.ipynb, 5816 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\model-checkpoint.ipynb, 26510 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\model_skopt-checkpoint.ipynb, 6201 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\nomad-checkpoint.ipynb, 11207 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\stack-checkpoint.ipynb, 6008 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\.ipynb_checkpoints\xgmodel-checkpoint.ipynb, 7911 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\10kgbt.csv, 25328 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\DMLP.ipynb, 22418 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\NUTILS.py, 3369 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\__pycache__, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\__pycache__\NUTILS.cpython-36.pyc, 3735 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\best, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\best\.ipynb_checkpoints, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\best\.ipynb_checkpoints\dnn_keras_f-checkpoint.ipynb, 10527 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\best\dnn_keras_f.ipynb, 10527 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\best\k_dnn_12.csv, 14828 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\best\k_model0473_1.h5, 14687432 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\catmodel.ipynb, 7097 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\checkpoint, 95 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\corr_script.py, 1421 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\cvmodel_set.ipynb, 4840 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\elemental-properties, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\elemental-properties\EA.csv, 47 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\elemental-properties\HOMO.csv, 37 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\elemental-properties\IP.csv, 46 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\elemental-properties\LUMO.csv, 40 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\elemental-properties\electronegativity.csv, 809 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\elemental-properties\mass.csv, 1134 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\elemental-properties\rd_max.csv, 38 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\elemental-properties\rp_max.csv, 37 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\elemental-properties\rs_max.csv, 30 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\test.csv, 47272 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\test_full.csv, 287184 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\test_prepared.csv, 171913 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\train.csv, 218636 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\train_full.csv, 1187383 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data\train_prepared.csv, 721471 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data_pipeline.py, 2281 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\data_scaled_1hot.csv, 493348 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\dnn_keras (1).ipynb, 1285300 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\dnn_keras.ipynb, 11284 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\dnn_keras_f.ipynb, 389362 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\.ipynb_checkpoints, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\.ipynb_checkpoints\meta_regressor-checkpoint.ipynb, 8877 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\.ipynb_checkpoints\new_bag-checkpoint.ipynb, 7029 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\.ipynb_checkpoints\new_cat-checkpoint.ipynb, 3486 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\.ipynb_checkpoints\new_knn-checkpoint.ipynb, 3195 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\.ipynb_checkpoints\new_lgbm-checkpoint.ipynb, 4624 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\.ipynb_checkpoints\new_xgb-checkpoint.ipynb, 5112 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\NUTILS.py, 3369 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\__pycache__, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\__pycache__\NUTILS.cpython-36.pyc, 3900 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\architecture, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\best, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\best\cat_2.csv, 25324 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\best\k_dnn_12.csv, 14828 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\best\sub.csv, 25366 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\best\xgb_2.csv, 25320 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\cat_models, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\cat_models\cat_band_model1, 468364 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\cat_models\cat_form_model1, 531352 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\corr_script.py, 1421 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\sample_submission.csv, 10739 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test.csv, 47272 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\.DS_Store, 6148 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\1, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\1\geometry.xyz, 5550 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\10, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\10\geometry.xyz, 5549 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\100, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\100\geometry.xyz, 1695 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\101, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\101\geometry.xyz, 5550 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\102, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\102\geometry.xyz, 1683 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\103, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\103\geometry.xyz, 5549 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\104, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\104\geometry.xyz, 2993 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\105, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\105\geometry.xyz, 2992 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\106, 0 , 2018-03-12
Kaggle-Nomad2018-master\NOMAD\ensembling\input\test\106\geometry.xyz, 2343 , 2018-03-12

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  • MNK
    This program is designed to find the parameters of the method of least squares, building eksperimetalnyh and theoretical curves for to assess the quality of approximation for propagation occurs trend of all their characteristics: propagation time, phase, amplitude and polarization. On the smooth running pa - ters inevitably superimposed random fluctuations. For devices they associated with the noise of radio circuits, to propagate in the Natural governmental environments, radio waves to the presence of turbulent fluctuations. Code is of interest mainly to radiophysicists(This program is designed to find the parameters of the method of least squares, building eksperimetalnyh and theoretical curves for to assess the quality of approximation for propagation occurs trend of all their characteristics: propagation time, phase, amplitude and polarization. On the smooth running pa- ters inevitably superimposed random fluctuations. For devices they associated with the noise of radio circuits, to propagate in the Natural governmental environments, radio waves to the presence of turbulent fluctuations. Code is of interest mainly to radiophysicists)
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