TestX | Various testing cases for personal learning

by fwhezfwhez Go Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | TestX Summary

TestX is a Go library. TestX has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Various testing cases for personal learning

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TestX has a low active ecosystem.

It has 9 star(s) with 5 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

TestX has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TestX is current.

Quality

TestX has no bugs reported.

Security

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

License

TestX does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

TestX releases are not available. You will need to build from source code and install.

Top functions reviewed by kandi - BETA

kandi has reviewed TestX and discovered the below as its top functions. This is intended to give you an instant insight into TestX implemented functionality, and help decide if they suit your requirements.

main function
GetSlotSlice converts a slice of mediums to a Slot slice
TestNotify test notification
once will send an HTTP request to the server
TestOrderInsert tests order insert
PourData pushes data to the data
Check order list
handlePayInfo return PayParam struct
InsertSlot2 insert new slot
MediumInsert inserts a new medium

Get all kandi verified functions for this library.

TestX Key Features

No Key Features are available at this moment for TestX.

TestX Examples and Code Snippets

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Community Discussions

Trending Discussions on TestX

how to solve "ValueError: Input 0 is incompatible with layer model: expected shape=(None, 16, 16, 3), found shape=(16, 16, 3)"?

Excel Data to XML

Point belonging to a polygon : program in C and python

Several Image Inputs to same ResNet Resulting in Unmatched Inputs

Sklearn -> Using Precision Recall AUC as a scoring metric in cross validation

how to add label of graphs string values like first layer second layer third instead of layer numbers

ValueError: Input 0 of layer conv2d_46 is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: (None, 64, 64)

accuracy: precision: recall: 0.9020 are always the same

rxjs publishReplay with windowTime parameter reseting

KNN prediction with L1 (Manhattan distance)

QUESTION

how to solve "ValueError: Input 0 is incompatible with layer model: expected shape=(None, 16, 16, 3), found shape=(16, 16, 3)"?

Asked 2021-Jun-07 at 20:04

I am trying to add tf.data pipeline to a regression task. The code starts with reading in the continues values using the csv file and inputting the images with cv2.imread. I split the data to train, test and validation using sklearn preprocessing.

...

ANSWER

Answered 2021-Jun-07 at 17:36

This error indicates that the expected dimension has not been passed to the model. The first dimension model expects is the batch. So, batch your data before pass it to the model.fit() like this:

Source https://stackoverflow.com/questions/67871408

QUESTION

Excel Data to XML

Asked 2021-Jun-04 at 13:23

Need to convert excel data into XML format.

...

ANSWER

Answered 2021-Jun-04 at 10:36

For a simple case one way would be to build the xml line by line

Source https://stackoverflow.com/questions/67832855

QUESTION

Point belonging to a polygon : program in C and python

Asked 2021-May-28 at 15:23

I have to write an algorithm in C and Python to know if a point belongs to a polygon or not (all entered by the user). My algorithm works in python but the C one doesn't work and I can't find my errors since my debugger code blocks refuse to work. So if someone could tell me where my error is, I would be very grateful...

The user is asked for the number of vertices of the polygon n≥3 (while). We ask for their coordinates (For) which we store in 2 arrays (CoordX/CoordY). The coordinates of the point to be tested are requested (TestX/TestY). We find the equations of lines connecting the points that follow each other (For). General case: we look for a and b of y = ax + b. a= (y(i+1) - y(i))/(x(i+1) -x(i)); b=y-ax

Special case: if the point at x, (i+1) - i = 0 then we have a line of equation y = x = k (constant) with b = 0; Line = True. if the point at y, (i+1) - i = 0 then we have a line with equation y = b with a = 0.

We determine if the point is between the bounds y(i) and y(i+1) (For) in order to know which line will cross the point to be tested: Bound = True/False, which we store in an array of Booleans, TabBorne.

We count the number of lines that the point to be tested will cross if we tend x towards infinity (For); we increment k (we take care to use only the lines that we will cross (If)).

y=ax+b<=>x= (y-b)/a,a≠0

Special case: if a point lies on a vertex, Vertex = True and the point lies in the figure.

We count the number of lines touched % 2 : If = 1, it is inside the polygon; Otherwise, it is outside.

In Python:

...

ANSWER

Answered 2021-May-28 at 15:23

The first problem is in this part of the code:

Source https://stackoverflow.com/questions/67738469

QUESTION

Several Image Inputs to same ResNet Resulting in Unmatched Inputs

Asked 2021-May-25 at 08:33

I am trying to build a network where ResNet does feature detection seperately on three input images. After feature detection the three parallel branches get combined with dense layers. An error gets thrown when trying to give the model some input.

...

ANSWER

Answered 2021-May-24 at 06:38

I think you need to have

Source https://stackoverflow.com/questions/67667357

QUESTION

Sklearn -> Using Precision Recall AUC as a scoring metric in cross validation

Asked 2021-May-25 at 02:10

I would like to use the AUC for the Precision and Recall curve as a metric to train my model. Do I need to make a specific scorer for this when using cross validation?

Consider the below reproducible example. Note the imbalanced target variable.

...

ANSWER

Answered 2021-May-25 at 02:10

"Average precision" is what you probably want, measuring a non-interpolated area under the PR curve. See the last few paragraphs of this example and this section of the User Guide.

For the scorer, use "average_precision"; the metric function is average_precision_score.

Source https://stackoverflow.com/questions/67678705

QUESTION

how to add label of graphs string values like first layer second layer third instead of layer numbers

Asked 2021-May-24 at 10:21

# prepare dataset
trainX, trainy, testX, testy = create_dataset()

# evaluate model and plot learning curve with given number of nodes
num_nodes = [1, 2, 3, 4, 5, 6, 7]

for n_nodes in num_nodes:
    # evaluate model with a given number of nodes
    history, result = evaluate_model(
        n_nodes,
        trainX,
        trainy,
        testX,
        testy,
    )

    # summarize final test set accuracy
    print('nodes=%d: %.3f' % (n_nodes, result))

    # plot learning curve
    pyplot.plot(history.history['accuracy'], label=str(n_nodes))

...

ANSWER

Answered 2021-May-23 at 11:29

If you know your names ahead of time:

Source https://stackoverflow.com/questions/67658795

QUESTION

ValueError: Input 0 of layer conv2d_46 is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: (None, 64, 64)

Asked 2021-May-21 at 14:44

I am using input images with (64, 64, 3) shape based on the following script. I am not sure why it returns error about data dimension. I also tried trainX = tf.expand_dims(trainX, axis=-1) based on this post, but I could not solve it. Can anyone help me about that?

...

ANSWER

Answered 2021-May-21 at 14:44

Simply change the following line:

Source https://stackoverflow.com/questions/67635325

QUESTION

accuracy: precision: recall: 0.9020 are always the same

Asked 2021-May-09 at 22:25

I have in my test dataset 5960 images: and I got results metrics:

...

ANSWER

Answered 2021-May-09 at 22:16

When you apply one-hot-encoding for binary classification these metrics mess up. Here is an example:

Your labels looks like this after one hot encoding: [ ... [1,0], [0,1], [1,0] ... ]

If you pay attention your TP equals TN. When it is correctly predicted as class 0, it is TP for class 0, also TN for class 1. That's why they are equal.

Don't apply one hot encoding and change:

Source https://stackoverflow.com/questions/67462188

QUESTION

rxjs publishReplay with windowTime parameter reseting

Asked 2021-Apr-23 at 23:24

I'm trying to limit the time subscriptions are cached in rxjs. Previously the caching was done with pipe(publishReplay(1), refCount()). After finding this nice answer and reading the docs, I found out that this cache time can be limited by passing a second parameter to publishReplay. Example: publishReplay(1, 60 * 1000)

I tried to make a minimal example:

...

ANSWER

Answered 2021-Apr-23 at 23:24

After 3 Hours of debugging, I found the following answer: https://stackoverflow.com/a/54957061

All I had to do, was add a take(1) at the end.

New example:

Source https://stackoverflow.com/questions/67221252

QUESTION

KNN prediction with L1 (Manhattan distance)

Asked 2021-Apr-22 at 15:25

I can run a KNN classifier with the default classifier (L2 - Euclidean distance):

...

ANSWER

Answered 2021-Apr-22 at 15:25

The metric has to be passed as a string.

Source https://stackoverflow.com/questions/67215974

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