ludwig-ai--ludwig
wehub-resource-sync
593b94c120
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66 行
2.0 KiB
Python
66 行
2.0 KiB
Python
#! /usr/bin/env python
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# Copyright (c) 2023 Predibase, Inc., 2019 Uber Technologies, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import math
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import numpy as np
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def softmax(x, temperature=1.0):
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e_x = np.exp((x - np.max(x)) / temperature)
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return e_x / e_x.sum()
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def int_type(number):
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if number <= np.iinfo(np.int8).max:
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return np.int8
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elif number <= np.iinfo(np.int16).max:
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return np.int16
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elif number <= np.iinfo(np.int32).max:
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return np.int32
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else: # if number <= np.iinfo(np.int64).max:
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return np.int64
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def convert_size(size_bytes):
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if size_bytes == 0:
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return "0B"
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size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
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i = int(math.floor(math.log(size_bytes, 1024)))
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p = math.pow(1024, i)
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s = round(size_bytes / p, 2)
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return f"{s} {size_name[i]}"
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def round2precision(val, precision: int = 0, which: str = ""):
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if precision < 0:
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raise ValueError(f"precision must be non-negative, got {precision}")
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val *= 10**precision
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round_callback = round
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if which.lower() == "up":
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round_callback = math.ceil
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if which.lower() == "down":
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round_callback = math.floor
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return "{1:.{0}f}".format(precision, round_callback(val) / 10**precision)
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def cumsum(x: list[int]) -> list[int]:
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results = []
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j = 0
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for i in range(0, len(x)):
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j += x[i]
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results.append(j)
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return results
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