score | ossia score , an interactive sequencer | Audio Utils library
kandi X-RAY | score Summary
kandi X-RAY | score Summary
ossia score is a sequencer for audio-visual artists, designed to create interactive shows. Sequence OSC, MIDI, DMX, sound, video and more, between multiple software and hardware, create interactive and intermedia scores and script with JavaScript, PureData or C++ to customize your score. Free, open source and runs on desktop, mobile, web and embedded.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of score
score Key Features
score Examples and Code Snippets
Lines of Code : 46License : Permissive (MIT License)
Copyprivate int calculateFinalScore(Board board) {
List> rowsToScore = new ArrayList<>();
for (int i = 0; i < board.getSize(); ++i) {
List row = new ArrayList<>();
List col = new ArrayList<> public int miniMax(int depth, boolean isMaximizer, int index, boolean verbose) {
int bestScore, score1, score2;
if (depth == height) { // Leaf node reached.
return scores[index];
}
score1 = miniMax(depth def solution():
"""Returns the total of all the name scores in the file.
>>> solution()
871198282
"""
with open(os.path.dirname(__file__) + "/p022_names.txt") as file:
names = str(file.readlines()[0])
nam Community Discussions
Trending Discussions on score
QUESTION
I am using Perceptual hashing technique to find near-duplicate and exact-duplicate images. The code is working perfectly for finding exact-duplicate images. However, finding near-duplicate and slightly modified images seems to be difficult. As the difference score between their hashing is generally similar to the hashing difference of completely different random images.
To tackle this, I tried to reduce the pixelation of the near-duplicate images to 50x50 pixel and make them black/white, but I still don't have what I need (small difference score).
This is a sample of a near duplicate image pair:
Image 1 (a1.jpg):
Image 2 (b1.jpg):
The difference between the hashing score of these images is : 24
When pixeld (50x50 pixels), they look like this:
rs_a1.jpg
rs_b1.jpg
The hashing difference score of the pixeled images is even bigger! : 26
Below two more examples of near duplicate image pairs as requested by @ann zen:
Pair 1
Pair 2
The code I use to reduce the image size is this :
...ANSWER
Answered 2022-Mar-22 at 12:48Rather than using pixelisation to process the images before finding the difference/similarity between them, simply give them some blur using the cv2.GaussianBlur() method, and then use the cv2.matchTemplate() method to find the similarity between them:
QUESTION
Im attempting to find model performance metrics (F1 score, accuracy, recall) following this guide https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/
This exact code was working a few months ago but now returning all sorts of errors, very confusing since i havent changed one character of this code. Maybe a package update has changed things?
I fit the sequential model with model.fit, then used model.evaluate to find test accuracy. Now i am attempting to use model.predict_classes to make class predictions (model is a multi-class classifier). Code shown below:
...ANSWER
Answered 2021-Aug-19 at 03:49This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference
update to
QUESTION
In my dataframe, I have multiple columns with student grades. I would like to sum the "Quiz" columns (e.g., Quiz1, Quiz2). However, I only want to sum the top 2 values, and ignore the others. I want to create a new column with the total (i.e., the sum of the top 2 values). There is also the issue of having grades that tie for the top 2 grades in a given row. For example, Aaron has a high score of 42, but then there are two scores that tie for the second highest (i.e., 36).
Data
...ANSWER
Answered 2021-Dec-12 at 23:25QUESTION
"I was doing the follow exercise:
A gymnast can earn a score between 1 and 10 from each judge.
Print out a series of sentences, "A judge can give a gymnast _ points." Don't worry if your first sentence reads "A judge can give a gymnast 1 points." However, you get 1000 bonus internet points if you can use a for loop, and have correct grammar.
My code is this:
...ANSWER
Answered 2022-Feb-17 at 03:04Conditionally add the s to the string:
QUESTION
I run sample JHM benchmark which suppose to show dead code elimination. Code is rewritten for conciseness from jhm github sample.
...ANSWER
Answered 2022-Feb-09 at 17:17Those samples depend on JDK internals.
Looks like since JDK 9 and JDK-8152907, Math.log is no longer intrinsified into C2 intermediate representation. Instead, a direct call to a quick LIBM-backed stub is made. This is usually faster for the code that actually uses the result. Notice how measureCorrect is faster in JDK 17 output in your case.
But for JMH samples, it limits the the compiler optimizations around the Math.log, and dead code / folding samples do not work properly. The fix it to make samples that do not rely on JDK internals without a good reason, and instead use a custom written payload.
This is being done in JMH here:
QUESTION
Looking into UTF8 decoding performance, I noticed the performance of protobuf's UnsafeProcessor::decodeUtf8 is better than String(byte[] bytes, int offset, int length, Charset charset) for the following non ascii string: "Quizdeltagerne spiste jordbær med flØde, mens cirkusklovnen".
I tried to figure out why, so I copied the relevant code in String and replaced the array accesses with unsafe array accesses, same as UnsafeProcessor::decodeUtf8.
Here are the JMH benchmark results:
ANSWER
Answered 2022-Jan-12 at 09:52To measure the branch you are interested in and particularly the scenario when while loop becomes hot, I've used the following benchmark:
QUESTION
The dataframe looks like this
...ANSWER
Answered 2022-Jan-16 at 18:49With apply, use MARGIN = 1, to loop over the rows on the numeric columns, sort, get the head/tail depending on decreasing = TRUE/FALSE and return with the mean in base R
QUESTION
I've converted a Keras model for use with OpenVino. The original Keras model used sigmoid to return scores ranging from 0 to 1 for binary classification. After converting the model for use with OpenVino, the scores are all near 0.99 for both classes but seem slightly lower for one of the classes.
For example, test1.jpg and test2.jpg (from opposite classes) yield scores of 0.00320357 and 0.9999, respectively.
With OpenVino, the same images yield scores of 0.9998982 and 0.9962392, respectively.
Edit* One suspicion is that the input array is still accepted by the OpenVino model but is somehow changed in shape or "scrambled" and therefore is never a match for class one? In other words, if you fed it random noise, the score would also always be 0.9999. Maybe I'd have to somehow get the OpenVino model to accept the original shape (1,180,180,3) instead of (1,3,180,180) so I don't have to force the input into a different shape than the one the original model accepted? That's weird though because I specified the shape when making the xml and bin for openvino:
...ANSWER
Answered 2022-Jan-05 at 06:06Generally, Tensorflow is the only network with the shape NHWC while most others use NCHW. Thus, the OpenVINO Inference Engine satisfies the majority of networks and uses the NCHW layout. Model must be converted to NCHW layout in order to work with Inference Engine.
The conversion of the native model format into IR involves the process where the Model Optimizer performs the necessary transformation to convert the shape to the layout required by the Inference Engine (N,C,H,W). Using the --input_shape parameter with the correct input shape of the model should suffice.
Besides, most TensorFlow models are trained with images in RGB order. In this case, inference results using the Inference Engine samples may be incorrect. By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with --reverse_input_channels argument.
I suggest you validate this by inferring your model with the Hello Classification Python Sample instead since this is one of the official samples provided to test the model's functionality.
You may refer to this "Intel Math Kernel Library for Deep Neural Network" for deeper explanation regarding the input shape.
QUESTION
I need to write an algorithm where the user selects an option to filter a particular data and on every click it makes an api request returning the filtered data. As many times the user clicks it needs to continue to filter out the data. My question is, how can the algorithm save the most recent result and run a for/filter loop on the most recent results? Should I store the most recent results in localStorage in order to further filter the results? And when the user decides to unselect the data that they wanted to filter, it should show the user their previous results. The user should be able to continue to filter until they get the desired data. Please see example below.
...ANSWER
Answered 2021-Dec-28 at 18:47You can iterate the array and check if each object satisfies the passed filters. This works with filters having multiple elements and each element having multiple properties.
QUESTION
I am trying code from this page. I ran up to the part LR (tf-idf) and got the similar results
After that I decided to try GridSearchCV. My questions below:
1)
...ANSWER
Answered 2021-Dec-09 at 23:12You end up with the error with precision because some of your penalization is too strong for this model, if you check the results, you get 0 for f1 score when C = 0.001 and C = 0.01
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install score
Official Win/Mac/Linux releases:
ArchLinux, OpenSUSE, Fedora, Debian, WASM, Raspberry Pi:
Ubuntu Bionic, Focal on GCC, Clang:
macOS (with Brew):
ossia score uses CppDepend to ensure consistent code quality improvements ; please check it out !
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
