testy | silly c test framework | Functional Testing library
kandi X-RAY | testy Summary
kandi X-RAY | testy Summary
This is a real bad c test framework that has 1 possibly redeeming quality:. Found the idea in this nice writeup, see link below or testy.h.
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QUESTION
The following code results in None () across the row in every attempt. The query.values()
code below is just a shortened line so as to keep things less complicated. Additionally I have problems inserting a dict as JSON in the address fields but that's another question.
ANSWER
Answered 2021-Jun-12 at 05:17I am not sure what do you mean with
The query.values() code below is just a shortened line so as to keep things less complicated.
So maybe I am not understanding the issue properly.
At any case the problem here is that you execute the insert()
and the values()
separately, while it is meant to be "chained".
Doing something like:
QUESTION
I'm writing a small script to map and rename netword drives. I wanted to use variables (user input) in drive letter, but for some reason the script won't accept anything but static drive letters. Please help
...ANSWER
Answered 2021-Jun-07 at 09:02Persistent drives MUST be named with letter.
-Name
parameter is described:
Specifies a name for the new drive. For persistent mapped network drives, use a drive letter. For temporary PowerShell drives, you aren't limited to drive letters, use any valid string.
Check -Persist
parameter here https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/new-psdrive?view=powershell-5.1#parameters
QUESTION
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 17:36This 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:
QUESTION
In a large corpus of text, I am interested in extracting every sentence which has a specific list of (Verb-Noun) or (Adjective-Noun) somewhere in the sentence. I have a long list but here is a sample. In my MWE I am trying to extract sentences with "write/wrote/writing/writes" and "book/s". I have around 30 such pairs of words.
Here is what I have tried but it's not catching most of the sentences:
...ANSWER
Answered 2021-May-29 at 08:53The issue is that in the Matcher, by default each dictionary in the pattern corresponds to exactly one token. So your regex doesn't match any number of characters, it matches any one token, which isn't what you want.
To get what you want, you can use the OP
value to specify that you want to match any number of tokens. See the operators or quantifiers section in the docs.
However, given your problem, you probably want to actually use the Dependency Matcher instead, so I rewrote your code to use that as well. Try this:
QUESTION
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:
QUESTION
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 06:38I think you need to have
QUESTION
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
.
QUESTION
# 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 11:29If you know your names ahead of time:
QUESTION
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 14:44Simply change the following line:
QUESTION
I have in my test dataset 5960
images:
and I got results metrics:
ANSWER
Answered 2021-May-09 at 22:16When 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:
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