cv | My CV built using RMarkdown and the pagedown package | GCP library

 by   nstrayer HTML Version: 1.0 License: No License

kandi X-RAY | cv Summary

kandi X-RAY | cv Summary

cv is a HTML library typically used in Cloud, GCP, React applications. cv has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

As I get older and more crotchety I find it more and more difficult to manually update a CSV. In response to this, I have moved the data-storing mechanism from a plain CSV to google sheets using the wonderful googlesheets4 package. This allows for a much more easy updating system and also makes it easy to store all the other info that didn't feel write to put into a CSV before (like the intro and aside text) right with everything as separate pages/sheets within the main sheet.
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              cv has a low active ecosystem.
              It has 500 star(s) with 372 fork(s). There are 16 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 11 open issues and 4 have been closed. On average issues are closed in 1 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of cv is 1.0

            kandi-Quality Quality

              cv has no bugs reported.

            kandi-Security Security

              cv has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              cv does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              cv releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.

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            cv Key Features

            No Key Features are available at this moment for cv.

            cv Examples and Code Snippets

            No Code Snippets are available at this moment for cv.

            Community Discussions

            QUESTION

            Invalid Character when Selecting classname - Python Webscraping
            Asked 2021-Jun-16 at 01:11

            I am beginning to learn the basics of webscraping with Python, but I am having a little trouble with my code. I am trying to scrape the weather from the front page of 'yahoo.com':

            ...

            ANSWER

            Answered 2021-Jun-16 at 01:11

            The problem is that your CSS selectors include parentheses () and dollar signs $. These symbols already have a special meaning. See:

            You can escape these characters using a backslash \.

            Source https://stackoverflow.com/questions/67994434

            QUESTION

            What is the Sobel operator?
            Asked 2021-Jun-15 at 18:13

            I tried 5 different implementations of the Sobel operator in Python, one of which I implemented myself, and the results are radically different.

            My questions is similar to this one, but there are still differences I don't understand with the other implementations.

            Is there any agreed on definition of the Sobel operator, and is it always synonymous to "image gradient"?

            Even the definition of the Sobel kernel is different from source to source, according to Wikipedia it is [[1, 0, -1],[2, 0, -2],[1, 0, -1]], but according to other sources it is [[-1, 0, 1],[-2, 0, 2],[-1, 0, 1]].

            Here is my code where I tried the different techniques:

            ...

            ANSWER

            Answered 2021-Jun-15 at 14:22

            according to wikipedia it's [[1, 0, -1],[2, 0, -2],[1, 0, 1]] but according to other sources it's [[-1, 0, 1],[-2, 0, 2],[-1, 0, 1]]

            Both are used for detecting vertical edges. Difference here is how these kernels mark "left" and "right" edges.

            For simplicity sake lets consider 1D example, and let array be

            [0, 0, 255, 255, 255]

            then if we calculate using padding then

            • kernel [2, 0, -2] gives [0, -510, -510, 0, 0]
            • kernel [-2, 0, 2] gives [0, 510, 510, 0, 0]

            As you can see abrupt increase in value was marked with negative values by first kernel and positive values by second. Note that is is relevant only if you need to discriminate left vs right edges, when you want just to find vertical edges, you might use any of these 2 aboves and then get absolute value.

            Source https://stackoverflow.com/questions/67987368

            QUESTION

            Read/write Eigen::Matrix with cv::Filestorage
            Asked 2021-Jun-15 at 15:05

            According to the OpenCV Docs, we can use cv::FileStorage to read/write custom data structure from/to config files (XML, YAML, JSON):

            ...

            ANSWER

            Answered 2021-Jun-15 at 15:05

            The issue is due to the intruduction of namespace, indeed you can get a similar issue with this code:

            Source https://stackoverflow.com/questions/67985606

            QUESTION

            calling a __host__ function from a __host__ __device__ functon is not allowed
            Asked 2021-Jun-14 at 14:06

            I am trying to use thrust with Opencv classes. The final code will be more complicated including using device memory but this simple example does not build successfully.

            ...

            ANSWER

            Answered 2021-Jun-14 at 14:06

            As pointed out in the comments, for the code you have shown, you are getting a warning and this warning can be safely ignored.

            For usage in CUDA device code:

            For a C++ class to be usable in CUDA device code, any relevant member functions that will be used explicitly or implicitly in CUDA device code, must be marked with the __device__ decorator. (There are a few exceptions e.g. for defaulted constructors which don't apply here.)

            The OpenCV class you are attempting to use (cv::KeyPoint), doesn't meet these requirements for use in device code. It won't be usable as-is.

            There may be a few options:

            1. Recast your work using cv::KeyPoint to use some class that provides similar functionality, that you write yourself, in such a way as to be properly designed and decorated.

            2. Perhaps see if OpenCV built with CUDA has an alternate version here (properly designed/decorated) (my guess would be it probably doesn't)

            3. Rewrite OpenCV itself, taking into account all necessary design changes to allow the cv::KeyPoint class to be usable in device code.

            4. As a variant of suggestion 1, copy the relevant data .response to a separate set of classes or just a bare array, and do your selection work based on that. The selection work done there can be used to "filter" the original array.

            Source https://stackoverflow.com/questions/67966538

            QUESTION

            i am using open CV and i am facing this error
            Asked 2021-Jun-14 at 13:05
            import cv2
            import numpy as np
            frameWidth = 640
            frameHeight = 480
            
            cap = cv2.VideoCapture(0)
            cap.set(3,frameWidth)
            cap.set(4,frameHeight)
            cap.set(10,150)
            
            myColors=[[5,107,0,19,255,255],
                     [133,56,0,159,156.255],
                     [57,76,0,100,255,255]]
            
            def findColors(img,myColors):
                imgHSV = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
                for color in myColors:
                    lower =  np.array(color[0:3])
                    upper =  np.array(color[3:6])
                    mask  = cv2.inRange(imgHSV, lower, upper)
                    cv2.imshow(str(color[0]),mask)
            
            while True:
                success, img = cap.read()
                findColors(img,myColors)
                cv2.imshow("result", img)
                if cv2.waitKey(1) & 0xFF == ord ('q'):
                    break
            
            ...

            ANSWER

            Answered 2021-Jun-12 at 23:04

            there is a typo in your code.

            Source https://stackoverflow.com/questions/67952679

            QUESTION

            How to push a document in an array nested in another array?
            Asked 2021-Jun-14 at 09:31

            This is my problem : I have a document ( let's call it root) containing an array of another documents (stick), that contain another array of another documents (leaf).

            Or simply said : root{ stickChain[leaveschain1[ leaf1, leaf2],leaveschain2[ leaf1, leaf2] ]}

            I have access to root _id, stick _id, or whatever it is needed to identify each document.

            Basically, the best result I've come so far is, when creating my leaves documents, is to store then at the same level tha sticks, or in another word I've come to create an array of leaves in root.

            I'm working in javascript and using mongoose

            This is the line I've used:

            ...

            ANSWER

            Answered 2021-Jun-14 at 09:31

            I've splitted my dument like this :

            • root[stickChain _id]
            • stick[leavesChain[leaf]]

            Thanks to Andrey Popov for his explications

            Source https://stackoverflow.com/questions/67917435

            QUESTION

            How to get indices of instances during cross-validation
            Asked 2021-Jun-13 at 17:04

            I am doing a binary classification. May I know how to extract the real indexes of the misclassified or classified instances of the training data frame while doing K fold cross-validation? I found no answer to this question here.

            I got the values in folds as described here:

            ...

            ANSWER

            Answered 2021-Jun-13 at 17:04

            From cross_val_predict you already have the predictions. It's a matter of subsetting your data frame where the predictions are not the same as your true label, for example:

            Source https://stackoverflow.com/questions/67956643

            QUESTION

            From train test split to cross validation in sklearn using pipeline
            Asked 2021-Jun-13 at 15:49

            I have the following piece of code:

            ...

            ANSWER

            Answered 2021-Jun-13 at 15:49

            Pipeline is used to assemble several steps such as preprocessing, transformations, and modeling. StratifiedKFold is used to split your dataset to assess the performance of your model. It is not meant to be used as a part of the Pipeline as you do not want to perform it on new data.

            Therefore it is normal to perform it out of the pipeline's structure.

            Source https://stackoverflow.com/questions/67956414

            QUESTION

            How to pass props to parent from child in React?
            Asked 2021-Jun-13 at 14:35

            I am new to React and I am trying to create a "CV-Generator" like this like this, What happens here is whenever user puts a value in any of the input it automatically renders to the Render preview(right side), My problem here is I am rendering a

            where it renders three child components namely:- , , , In each of three I process Input onChange and setState the data, but problem is I need to render this data to again Render Preview(right on the link), But I am confused how can I pass my data from child components(i.e. , , ) to the parent Component()

            App.js

            ...

            ANSWER

            Answered 2021-Jun-13 at 14:28

            You have to use callback in child, and then some function in parent to get those data. But remember that you have to trigger the callback function first.

            Source https://stackoverflow.com/questions/67959121

            QUESTION

            Simultaneous feature selection and hyperparameter tuning
            Asked 2021-Jun-13 at 14:19

            I'm trying to conduct both hyperparameter tuning and feature selection on a sklearn SVC model.

            I tried the below code, but am getting an error which I have included.

            ...

            ANSWER

            Answered 2021-Jun-13 at 14:19

            You want to perform a grid search over a Pipeline object. When defining the parameters for the different steps of the pipeline, you have to use the __ syntax:

            Source https://stackoverflow.com/questions/67958533

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

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            You can download it from GitHub.

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