trungnt13

import torch
import torch.nn as nn


def compute_scale_and_shift(prediction, target, mask):
    # system matrix: A = [[a_00, a_01], [a_10, a_11]]
    a_00 = torch.sum(mask * prediction * prediction, (1, 2))
    a_01 = torch.sum(mask * prediction, (1, 2))
    a_11 = torch.sum(mask, (1, 2))

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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#

trungnt13

#!/bin/bash
# Licensed to the Apache Software Foundation (ASF)
# https://github.com/apache/tvm/tree/main/docker/install

. /etc/profile

set -o errexit -o nounset
set -o pipefail

ANDROID_HOME=/opt/android-sdk-linux

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{"schemaVersion":1,"label":"Coverage","message":"79%","color":"yellow"}

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ID10001 m 112.3214
ID10002 m 140.6493
ID10003 m 146.4722
ID10004 m 120.6954
ID10005 m 150.4191
ID10006 f 165.4478
ID10007 f 270.6876
ID10008 f 225.5818
ID10009 m 200.2969
ID10010 m 102.2391

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# >>> conda initialize >>>
# !! Contents within this block are managed by 'conda init' !!
__conda_setup="$('/home/trung/miniconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
if [ $? -eq 0 ]; then
    eval "$__conda_setup"
else
    if [ -f "/home/trung/miniconda3/etc/profile.d/conda.sh" ]; then
        . "/home/trung/miniconda3/etc/profile.d/conda.sh"
    else
        export PATH="/home/trung/miniconda3/bin:$PATH"

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from __future__ import absolute_import, division, print_function

import os

import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from tensorflow import keras

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from __future__ import print_function, division, absolute_import

import tensorflow as tf
tf.enable_eager_execution()

from tensorflow_probability.python.distributions import NegativeBinomial
from zero_inflated import ZeroInflated

nb = NegativeBinomial(total_count=10, probs=0.5)
zfnb = ZeroInflated(dist=nb, pi=0.9)

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WC Classic Rock           1        6kN   16g   7/13.0mm
WC Classic Rock           2        10kN  25g   8.2/13.8mm
WC Classic Rock           3        10kN  28g   9.6/14.5mm
WC Classic Rock           4        10kN  30g   11.3/15.3mm
WC Classic Rock           5        10kN  32g   13.3/16.8mm
WC Classic Rock           6        10kN  36g   15.6/19.0mm
WC Classic Rock           7        10kN  38g   18.4/21.2mm
WC Classic Rock           8        12kN  47g   21.6/24.0mm
WC Classic Rock           9        12kN  60g   24.4/27.7mm
WC Classic Rock           10       12kN  71g   30.0/31.0mm

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Camalot X4 Red        0.1     5kN     51g    8.4-13.8mm
Camalot X4 Yellow     0.2     6kN     54g    9.9-16.5mm
Camalot X4 Blue       0.3     8kN     75g    12.4-21.2mm
Camalot X4 Grey       0.4     9kN     82g    15.5-26.6mm
Camalot X4 Purple     0.5     9kN     91g    19.8-33.7mm
Camalot X4 Green      0.75    9kN     112g   24-41.2mm

Camalot C3 Grey       000     4kN     55g    7.8-12.9mm
Camalot C3 Purple     00      6kN     57g    9.0-13.7mm
Camalot C3 Green      0       7kN     59g    10.7-15.8mm