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审判长 2025-02-21 01:02:11

在阅读上面的问题时，我认为根据您的要求，您需要称呼：

这将返回具有中心属性的ObjectInstancestate

空间图表将具有您要寻找的姿势信息。

如果您对混合现实中的一般坐标系统有疑问，我建议您从这里开始了解更多信息：
坐标系统 - 混合现实

另一个好资源 在AOA上，如果您尚未查看样本，请在此处提供：
https://github.com/azure/azure/azure-azure-object-andors

也有在此处浏览此示例的教程：

In reading your question above, I think based on what you are asking, you are needing to call this:
ObjectInstance.TryGetCurrentState Method (Microsoft.Azure.ObjectAnchors) | Microsoft Docs

This will return an ObjectInstanceState with a center property that is a
SpatialGraphLocation Struct (Microsoft.Azure.ObjectAnchors.SpatialGraph) | Microsoft Docs

The SpatialGraphLocation will have the pose information that you are looking for.

If you have questions on Coordinate Systems in general in Mixed Reality, I'd suggest you start here to learn more:
Coordinate systems - Mixed Reality

Another good resources on AOA, if you haven't looked at the sample already, is available here:
https://github.com/Azure/azure-object-anchors

There is also a tutorial that walks through this sample here:
Quickstart: In-depth MRTK walkthrough - Azure Object Anchors | Microsoft Docs

如何从检测到的objectinstance中获得位置？

┾廆蒐ゝ 2025-02-14 01:02:11 浏览 3 评论 0 收藏 0
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审判长 2025-02-20 09:51:49

问题是词典。密钥不能列表。

您需要为整数和字符串提供单独的键。

to_replace = {'1': 'remote', 1: 'remote', '0': 'in_lab', 0: 'in_lab'}
ort_raw['Location'] = ort_raw['Location'].replace(to_replace)

The issue is with the dictionary. Keys can't be lists.

you need to have separate keys for the integers and strings.

to_replace = {'1': 'remote', 1: 'remote', '0': 'in_lab', 0: 'in_lab'}
ort_raw['Location'] = ort_raw['Location'].replace(to_replace)

pandas.df.replace返回typeError：不可用的类型：＆＃x27; list＆＃x27;

a√萤火虫的光℡ 2025-02-13 09:51:49 浏览 0 评论 0 收藏 0
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审判长 2025-02-20 06:57:15

首先安装openssl

brew install openssl

然后

export PKG_CONFIG_PATH="/usr/local/opt/openssl@3/lib/pkgconfig"
export CPPFLAGS="-I/usr/local/opt/openssl@3/include"
export LDFLAGS="-L/usr/local/opt/openssl@3/lib"

例外消失

pip3 install pycurl
DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621
Collecting pycurl
  Using cached pycurl-7.45.2.tar.gz (234 kB)
  Preparing metadata (setup.py) ... done
Building wheels for collected packages: pycurl
  Building wheel for pycurl (setup.py) ... done
  Created wheel for pycurl: filename=pycurl-7.45.2-cp39-cp39-macosx_10_13_x86_64.whl size=144557 sha256=2c56c17f7987b8739333b8078c6d8b61ed5c8e5239ea182992bbe0eb64208970
  Stored in directory: /Users/xlla/Library/Caches/pip/wheels/23/b0/37/d2c3211ee738adfb8ec6b6e10aa00e78ebc4de363f862a12c5
Successfully built pycurl
Installing collected packages: pycurl
Successfully installed pycurl-7.45.2

first install openssl

brew install openssl

then

export PKG_CONFIG_PATH="/usr/local/opt/openssl@3/lib/pkgconfig"
export CPPFLAGS="-I/usr/local/opt/openssl@3/include"
export LDFLAGS="-L/usr/local/opt/openssl@3/lib"

the exception go away

pip3 install pycurl
DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621
Collecting pycurl
  Using cached pycurl-7.45.2.tar.gz (234 kB)
  Preparing metadata (setup.py) ... done
Building wheels for collected packages: pycurl
  Building wheel for pycurl (setup.py) ... done
  Created wheel for pycurl: filename=pycurl-7.45.2-cp39-cp39-macosx_10_13_x86_64.whl size=144557 sha256=2c56c17f7987b8739333b8078c6d8b61ed5c8e5239ea182992bbe0eb64208970
  Stored in directory: /Users/xlla/Library/Caches/pip/wheels/23/b0/37/d2c3211ee738adfb8ec6b6e10aa00e78ebc4de363f862a12c5
Successfully built pycurl
Installing collected packages: pycurl
Successfully installed pycurl-7.45.2

错误＆＃x27; openssl/crypto.h＆＃x27;使用PIP在Mac上安装Pycurl时找不到文件

呢古 2025-02-13 06:57:13 浏览 4 评论 0 收藏 0
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审判长 2025-02-19 16:33:02

您将错误的元素包装在“过滤器：

arr[key].count = pageviews.filter(ua => ua === '"' + el.ua + '"').length;

应该是：

arr[key].count = pageviews.filter(ua => '"' + ua + '"' === el.ua).length;

You're wrapping the wrong element in your `filter :

arr[key].count = pageviews.filter(ua => ua === '"' + el.ua + '"').length;

Should be:

arr[key].count = pageviews.filter(ua => '"' + ua + '"' === el.ua).length;

array.filter返回没有匹配，可能有字符串中引号的问题吗？

诗化ㄋ丶相逢 2025-02-12 16:33:02 浏览 1 评论 0 收藏 0
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审判长 2025-02-19 13:36:47

您可以使用：

from enum import Enum


class Ranks(Enum):
    BEGINNER = (0, {'will': 1, 'health': 1})
    MID = (1, {'will': 2, 'health': 2})
    ADVANCED = (2, {'will': 3, 'health': 3})

    def __init__(self, v1, v2):
        self.v1 = v1
        self.v2 = v2

    @property
    def raw_abilities(self):
        return self.v2

https://docs.pypython.org/3/library/3/library/ enum.html＃行星

You can use:

from enum import Enum


class Ranks(Enum):
    BEGINNER = (0, {'will': 1, 'health': 1})
    MID = (1, {'will': 2, 'health': 2})
    ADVANCED = (2, {'will': 3, 'health': 3})

    def __init__(self, v1, v2):
        self.v1 = v1
        self.v2 = v2

    @property
    def raw_abilities(self):
        return self.v2

https://docs.python.org/3/library/enum.html#planet

干净的分配有条件dict值的方法而不重复dict

只是我以为 2025-02-12 13:36:47 浏览 2 评论 0 收藏 0
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审判长 2025-02-17 19:59:27

我检查了代码，问题源于您期望生成器生成尺寸的图像 - 128x3x128x128（batch_size x Channels x image_dim x image_dim）。但是，您写了Convranspose2D操作的方式，事实并非如此。

我检查了中间层的输出，您的发电机正在生成维度的输出图像 - 128x3x80x80 ，它是大小不匹配的，因为您的鉴别器的预期输入图像 - > 128x3x3x128x128x128 。

以下是发电机的Convtranpose2D操作中中间输出的形状 -

torch.Size([128, 4096, 4, 4])
torch.Size([128, 2048, 5, 5])
torch.Size([128, 1024, 10, 10])
torch.Size([128, 512, 20, 20])
torch.Size([128, 256, 40, 40])
torch.Size([128, 3, 80, 80])

我建议您修改发电机的ConvtransPose2D参数，如下所示 -

class Generator(nn.Module):
    def __init__(self, channels_noise, channels_img, features_g):
        super(Generator, self).__init__()
        self.net = nn.Sequential(
            self._block(channels_noise, features_g*32, 4, 1, 0),
            self._block(features_g*32, features_g*16, 4, 2, 1),
            self._block(features_g*16, features_g*8, 4, 2, 1),
            self._block(features_g*8, features_g*4, 4, 2, 1),
            self._block(features_g*4, features_g*2, 4, 2, 1),
            nn.ConvTranspose2d(features_g*2, channels_img, kernel_size=4, stride=2, padding=1),
            # Output: N x channels_img x 64 x 64
            nn.Tanh(),
        )

    def _block(self, in_channels, out_channels, kernel_size, stride, padding):
        return nn.Sequential(
            nn.ConvTranspose2d(
                in_channels,
                out_channels,
                kernel_size,
                stride,
                padding,
                bias=False,
            ),
            #nn.BatchNorm2d(out_channels),
            nn.ReLU(),
        )

    def forward(self, x):
        return self.net(x)

这会产生> 128x3x128x128x128 。中间尺寸如下 -

torch.Size([128, 4096, 4, 4])
torch.Size([128, 2048, 8, 8])
torch.Size([128, 1024, 16, 16])
torch.Size([128, 512, 32, 32])
torch.Size([128, 256, 64, 64])
torch.Size([128, 3, 128, 128])

只需将生成器替换为此，您的代码应适用于dimension 3x128x128 的图像。

I checked the code and the issue stems from the fact that you are expecting your Generator to generate images of dimension - 128x3x128x128 (batch_size x channels x image_dim x image_dim). However, the way that you have written the ConvTranspose2d operations, that is not the case.

I checked the output from the intermediate layers and your generator is generating output images of dimension - 128x3x80x80 which is a size mismatch as your Discriminator is expected input images of dimension - 128x3x128x128.

Here are the shapes of the intermediate outputs from your Generator's ConvTranpose2d operations -

torch.Size([128, 4096, 4, 4])
torch.Size([128, 2048, 5, 5])
torch.Size([128, 1024, 10, 10])
torch.Size([128, 512, 20, 20])
torch.Size([128, 256, 40, 40])
torch.Size([128, 3, 80, 80])

I'd suggest, you modify your Generator's ConvTranspose2d parameters as follows -

class Generator(nn.Module):
    def __init__(self, channels_noise, channels_img, features_g):
        super(Generator, self).__init__()
        self.net = nn.Sequential(
            self._block(channels_noise, features_g*32, 4, 1, 0),
            self._block(features_g*32, features_g*16, 4, 2, 1),
            self._block(features_g*16, features_g*8, 4, 2, 1),
            self._block(features_g*8, features_g*4, 4, 2, 1),
            self._block(features_g*4, features_g*2, 4, 2, 1),
            nn.ConvTranspose2d(features_g*2, channels_img, kernel_size=4, stride=2, padding=1),
            # Output: N x channels_img x 64 x 64
            nn.Tanh(),
        )

    def _block(self, in_channels, out_channels, kernel_size, stride, padding):
        return nn.Sequential(
            nn.ConvTranspose2d(
                in_channels,
                out_channels,
                kernel_size,
                stride,
                padding,
                bias=False,
            ),
            #nn.BatchNorm2d(out_channels),
            nn.ReLU(),
        )

    def forward(self, x):
        return self.net(x)

This produces the required dimension of 128x3x128x128. The intermediate dimensions are as follows -

torch.Size([128, 4096, 4, 4])
torch.Size([128, 2048, 8, 8])
torch.Size([128, 1024, 16, 16])
torch.Size([128, 512, 32, 32])
torch.Size([128, 256, 64, 64])
torch.Size([128, 3, 128, 128])

Just replace the Generator with this one and your code should work for images of dimension 3x128x128.

如何增加python中DCGAN的图像尺寸？

哑剧 2025-02-10 19:59:27 浏览 2 评论 0 收藏 0
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审判长 2025-02-17 19:30:19

参见

See cross-posted question here

DENEB 3x3子图

山人契 2025-02-10 19:30:19 浏览 3 评论 0 收藏 0
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审判长 2025-02-17 18:03:29

可能会更容易

library(dplyr)
rows_update(a, b, by = 'V1')

使用 rows_update 或通过与 data.table.table 进行分配（：= ）， 从“ b”数据中的列（'v2'）的'a'

library(data.table)
setDT(a)[b, V2 := i.V2, on = .(V1)]

It may be easier with rows_update

library(dplyr)
rows_update(a, b, by = 'V1')

Or do an assign (:=) by joining with data.table, which updates the column 'V2' in 'a' by the column ('V2') from 'b' data

library(data.table)
setDT(a)[b, V2 := i.V2, on = .(V1)]

用相同名称的强制变量从y数据集覆盖

转身以后 2025-02-10 18:03:29 浏览 1 评论 0 收藏 0
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审判长 2025-02-17 12:33:35

我会这样这样做：

CREATE TABLE aux AS
SELECT Users.user_id, COUNT(Undostres.user_id) AS count
FROM Users 
LEFT OUTER JOIN Undostres USING (user_id)
GROUP BY Users.user_id;

我假设您有一个用户列举了所有用户，无论他们是否发表了任何评论。在这种情况下，左外的连接有帮助，因为如果没有给定用户的注释，则用户仍然是结果的一部分，计数为0。

I would do it this way:

CREATE TABLE aux AS
SELECT Users.user_id, COUNT(Undostres.user_id) AS count
FROM Users 
LEFT OUTER JOIN Undostres USING (user_id)
GROUP BY Users.user_id;

I am assuming you have a table Users that enumerates all your users, whether they have made any comments or not. The LEFT OUTER JOIN helps in this case, because if there are no comments for a given user, the user is still part of the result, and the COUNT is 0.

直方图使用数据表（SQL查询）

只为守护你 2025-02-10 12:33:35 浏览 3 评论 0 收藏 0
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审判长 2025-02-17 06:38:55

iiuc，使用 dtype = str 作为 read_table 的参数，以防止pandas推断数据类型：

df = pd.read_table('ID 00d0.txt', header=None, delim_whitespace=True, dtype=str, 
                   names=['0','ID',"B0","B1","B2","B3","B4","B5","B6","B7"])

输出：

>>> df
                     0    ID  B0  B1  B2  B3  B4  B5  B6  B7
0    1656033437.571007  00d0  A0  00  00  00  00  00  ff  01
1    1656033437.590747  00d0  30  00  00  00  00  00  ff  01
2    1656033437.610978  00d0  30  00  00  00  00  00  ff  01
3    1656033437.630766  00d0  30  00  00  00  00  00  ff  01
4    1656033437.650933  00d0  30  00  00  00  00  00  ff  01
..                 ...   ...  ..  ..  ..  ..  ..  ..  ..  ..
96   1656033439.490835  00d0  2f  00  00  00  00  00  ff  01
97    1656033439.51084  00d0  2f  00  00  00  00  00  fe  01
98   1656033439.530714  00d0  2f  00  00  00  00  00  ff  01
99   1656033439.550823  00d0  2f  00  01  00  00  00  ff  01
100  1656033439.570697  00d0  2f  00  01  00  00  00  ff  01

[101 rows x 10 columns]

您可以通过链接 .astype（ {'0'：float}）。

IIUC, use dtype=str as parameter of read_table to prevent Pandas infer the datatype:

df = pd.read_table('ID 00d0.txt', header=None, delim_whitespace=True, dtype=str, 
                   names=['0','ID',"B0","B1","B2","B3","B4","B5","B6","B7"])

Output:

>>> df
                     0    ID  B0  B1  B2  B3  B4  B5  B6  B7
0    1656033437.571007  00d0  A0  00  00  00  00  00  ff  01
1    1656033437.590747  00d0  30  00  00  00  00  00  ff  01
2    1656033437.610978  00d0  30  00  00  00  00  00  ff  01
3    1656033437.630766  00d0  30  00  00  00  00  00  ff  01
4    1656033437.650933  00d0  30  00  00  00  00  00  ff  01
..                 ...   ...  ..  ..  ..  ..  ..  ..  ..  ..
96   1656033439.490835  00d0  2f  00  00  00  00  00  ff  01
97    1656033439.51084  00d0  2f  00  00  00  00  00  fe  01
98   1656033439.530714  00d0  2f  00  00  00  00  00  ff  01
99   1656033439.550823  00d0  2f  00  01  00  00  00  ff  01
100  1656033439.570697  00d0  2f  00  01  00  00  00  ff  01

[101 rows x 10 columns]

You can convert column '0' to float by chaining .astype({'0': float}).

熊猫读取txt文件作为十六进制的int instad（str）

我一向站在原地 2025-02-10 06:38:55 浏览 1 评论 0 收藏 0
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审判长 2025-02-17 01:34:31

为了添加其他注释，这也是API密钥的最后数字。

To add to the other comments, it's also the last digits of your API KEY.

MailChimp.com中API调用的服务器前缀是什么？

尴尬癌患者 2025-02-10 01:34:31 浏览 6 评论 0 收藏 0
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审判长 2025-02-17 01:11:41

角生产构建被用作静态文件。它向后端服务器提出了API请求以进行CRUD操作。

您需要做的是在Angular Dist（很可能是Dist文件夹中的index.html文件）中使用静态文件。

在Django 中使用某种路由（建议为Django的API或Web应用程序一个前缀，以了解您的要求。请查看 this ））

或拥有独立的服务托管了一些可以使用角度的位置（看看 this ），让角处理路由。

Angular production build is served as a static file. it makes the api request to the backend server for the CRUD operations.

what you need to do is serve the static files in angular dist(most probably the index.html file in the dist folder) folder.

using some sort of routing in django (suggestion have a prefix for the API or web app for django to know what are you asking for. look at this)

or have a standalone service hosted some where to serve Angular site(have a look at this) and let angular handle the routing.

在浏览器中刷新的角度应用后：Django响应

棒棒糖 2025-02-10 01:11:41 浏览 4 评论 0 收藏 0
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审判长 2025-02-17 00:58:21

在这里打开两件事。
首先，不建议将文件存储在数据库中。最好使用存储或直接使用服务器的文件系统。

其次，通常根据您的策略（在服务器的文件系统，数据库或第三方存储）上上传和保存文件，然后如果用户未进行付款，将进行清理。您需要定义进行清理的条件，无论是因为用户上传文件并且在一定时间段内一直不活动，还是因为他们单击了特定的按钮，还是两者的组合。

要触发清理，您有不同的可能性：

  • 上传文件时，安排任务，例如使用 django-q 要检查一下上传文件后1小时，如果付款尚未完成，则将文件删除
  • 编写一个django命令每天都会触发cron作业触发的cron作业，从比1小时，
  • 您还可以使用Django会议工作，并定期扫描1小时尚未活动的会话，他们的付款将待定，并假设这些用户将不会继续付款并删除其上传的文件

Two things to unpack here.
First, it is not advised to store files in the database. It's better to use a storage or directly the server's filesystem.

Second, files are usually uploaded and saved according to your strategy (on the server's filesystem, the database or a third party storage), and then a cleanup would happen if the user hasn't proceeded with the payment. You need to define the conditions for the cleanup to happen, whether it's because the user has uploaded file and has been inactive for a certain period of time, or because they click on a specific button, or a combination of both.

To trigger the cleanup, you have different possibilities:

  • When uploading the files, schedule a task, for instance using django-q to check that, say, 1 hour after uploading the files, if the payment hasn't been completed, the files are deleted
  • Write a django command that gets triggered by a cron job every day that deletes the files of the user whose payment has been pending for more than 1 hour
  • You could also work with django-sessions and regularly scan for sessions that haven't been active for 1 hour, whose payment are pending and assume these users will not proceed with the payment and delete their uploaded files

在Django中，有什么方法可以在多个请求中访问上传的文件？

傾旎 2025-02-10 00:58:21 浏览 1 评论 0 收藏 0
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审判长 2025-02-16 13:20:15

请参阅@michael的非矢量化解决方案，

 d = {}; torch.tensor([d.setdefault(tuple(i.tolist()), e) for e, i in enumerate(t4)])

另一个非矢量化解决方案是

t4_list = t4.tolist(); torch.tensor(list(map(lambda x: t4_list.index(x), t4)))

See the non-vectorized solution from @Michael

 d = {}; torch.tensor([d.setdefault(tuple(i.tolist()), e) for e, i in enumerate(t4)])

Another non-vectorized solution is

t4_list = t4.tolist(); torch.tensor(list(map(lambda x: t4_list.index(x), t4)))

生成一维张量作为2D张量行的独特索引（保持订单和原始索引）

乖乖兔^ω^ 2025-02-09 13:20:15 浏览 4 评论 0 收藏 0
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审判长 2025-02-16 11:28:21

至少您在设置 binding.source 时犯了一个错误。在通常的实例属性的情况下，它应该是具有该属性的对象，在您的情况下，是“ config”的实例。对于静态属性，您无需设置 binding.source 。

At least you made a mistake in setting Binding.Source. In the case of usual instance property, it should be the object which has that property, in your case, the instance of "Config". In the case of static property, you don't need to set Binding.Source.

WPF 4.8与静态属性结合

山有枢 2025-02-09 11:28:21 浏览 2 评论 0 收藏 0
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