ground0state’s gists

ground0state / making-the-most-of-local-llms.ipynb

Created March 22, 2025 05:03 — forked from kyo-takano/making-the-most-of-local-llms.ipynb

ローカルLLMはこーやって使うの💢

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ground0state / skorch.ipynb

Created December 20, 2020 14:43

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ground0state / timer.py

Created September 3, 2020 13:07

	import time
	import logging
	from contextlib import contextmanager
	from typing import Optional


	@contextmanager
	def timer(name: str, logger: Optional[logging.Logger] = None):
	t0 = time.time()
	msg = f"[{name}] start"

ground0state / basic_logger.py

Last active September 3, 2020 13:05

	import logging

	def get_logger(out_file=None):
	logger = logging.getLogger()
	formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
	logger.handlers = []
	logger.setLevel(logging.INFO)

	stream_handler = logging.StreamHandler()
	stream_handler.setFormatter(formatter)

ground0state / seed_torch.py

Created September 3, 2020 13:00

	import os
	import random
	import numpy as np
	import torch

	def set_seed(seed: int = 42):
	random.seed(seed)
	np.random.seed(seed)
	os.environ["PYTHONHASHSEED"] = str(seed)
	torch.manual_seed(seed)

ground0state / layered_fold_wrapper.py

Created September 2, 2020 17:26

	class LayeredFoldWrapper(Dataset):
	def __init__(self, dataset, n_splits=5, fold=0, valid=False):
	self.dataset = dataset
	self.n_splits = n_splits
	self.fold = fold
	self.valid = valid
	self.valid_index = list(self._valid_index(len(dataset), n_splits, fold))
	self.train_index = list(set(range(len(dataset))) - set(self.valid_index))

	def __len__(self):

ground0state / silhouette_score.py

Last active May 7, 2020 03:44

	"""
	MIT License
	Copyright (c) 2017-2020 Packt, grouns0state
	https://github.com/PacktPublishing/Artificial-Intelligence-with-Python/blob/master/LICENSE
	"""
	from sklearn.cluster import KMeans
	from sklearn import metrics
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.datasets import make_blobs

ground0state / mean_shift.py

Last active May 7, 2020 03:44

	"""
	MIT License
	Copyright (c) 2017-2020 Packt, grouns0state
	https://github.com/PacktPublishing/Artificial-Intelligence-with-Python/blob/master/LICENSE
	"""
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.cluster import MeanShift, estimate_bandwidth
	from itertools import cycle
	from sklearn.datasets import make_blobs

ground0state / makesoundfile.py

Created April 9, 2020 17:03

Python を使った WAVE ファイルの処理 LISENCE: CC-BY-NC-ND

	"""
	喜多一,『プログラミング演習 Python 2019( コラム編 )』, 2020-02-13, http://hdl.handle.net/2433/245698
	LISENCE: CC-BY-NC-ND

	[参考資料]
	桂田祐史:Python を使った WAVE ファイルの処理、 http://nalab.mind.meiji.ac.jp/~mk/lecture/fourier-2018/python-sound/ (2018/12/3 アクセス)
	"""

	import numpy as np
	import wave

ground0state / power-spectral-density.py

Created January 23, 2020 15:07

This code is from https://org-technology.com/posts/power-spectral-density.html

	# https://org-technology.com/posts/power-spectral-density.html
	"""
	適当な信号を作成して periodogram と welch で PSD を推定してみます。
	下の例ではセグメント長 nseg はデフォルトで 256 なので、
	ちょうど n/4 の長さです。
	いくつかセグメント長を変えて推定した結果をプロットしています。
	なおオーバーラップ noverlap はデフォルトのままで nseg/2、
	つまり 50% オーバーラップして計算しています。
	periodogramでは窓関数はデフォルトではなし(Boxcar と同じ)、
	welch では Hanning です。

abetan ground0state