kandi X-RAY | knn Summary
kandi X-RAY | knn Summary
Download the latest source code from:
Top functions reviewed by kandi - BETA
- The main classifier
- Display usage message
- Test the test dataset
- Initialize the LSH Record
- Create a training dataset
- Prints usage
- Classify a record
- Hashing function
- Initialize the LSH dataset
- Display usage
- Train the training algorithm
- Hashes a single lsh record
- Returns a hash value for the given hash
- Returns the number of trainDS
- Performs training on the model
- Returns the size of trainDS
- Computes the hamming function
- Returns a string representation of this dataset
- Compute the euclidean distance
- Main training algorithm
- The cosine function
- Compares this object to the specified value
knn Key Features
knn Examples and Code Snippets
Trending Discussions on knn
I was going through a college assignment on KNN given in python and in that assignment there was one block of code where they delete X_train,Y_train,X_test and Y_test variables before assigning those variables to other data. And in the comments they added that it prevents memory issues....
ANSWERAnswered 2021-Jun-14 at 17:23
Both examples accomplish the same thing - they decrease the reference count of the value
"any_dataset" by one. Using
del does this explicitly, overwriting a variable does this implicitly. When a value has zero references to it, it will be garbage-collected at some point in the future.
This being the case, I can't see any "memory issues" being prevented by doing it one way or the other.
Further reading material:
I have some
CSV files. These files consist of some rows and columns. First, I filtered the file (after reading based on 2 conditions) and then calculate the
ANSWERAnswered 2021-Jun-13 at 16:33
IIUC, you can try:
I am fairly new to AWS and Sagemaker and have decided to follow some of the tutorials Amazon has to familiarize myself with it. I've been following this one (tutorial) and I've realized that it's an older tutorial using Sagemaker v1. I've been able to look up and change whatever is needed for the tutorial to work in v2 but I became stuck at this part for storing the training data in a S3 bucket to deploy the model....
ANSWERAnswered 2021-Jun-07 at 02:39
It looks like they've left some of the code out, or changed the terminology and left in predictions by accident. predictions is an object that is defined on this page https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-test-model.html
You'll have to work out what predictions is in your case.
I want to do a multiple imputation with IterativeImputer.
Here is the dataset (the original is from https://www.kaggle.com/jboysen/mri-and-alzheimers) :
The variables to impute are "educ" and "ses". As they are categorical I've choose to use a classifier (KNeighborsClassifier from sklearn). Predictors are continuous (except "sex").
This is the code :...
ANSWERAnswered 2021-Jun-05 at 18:31
I just understood why it does not works. It's because IterativeImputer works only for continuous variables. So, apparently you can't apply multiple imputation for continuous variables with IterativeImputer. There is discussion about this here.
I saw it's possible to do simple imputation with categorical variables in python. However, it does not seem possible to do multiple imputation with this type of variables (anyway, I did not find).
An error occurred while executing the KNN algorithm. I don't know where the error occurred. Can anyone help me? Please. There is a code below. I don't know why, but the code was cut....
ANSWERAnswered 2021-Jun-05 at 17:06
One line defines:
i'm using OpenMP for a kNN project. The two parallelized for loops are:...
ANSWERAnswered 2021-Jun-01 at 10:36
Why the 16 Threads case differs so much from the others? I'm running the algorithm on a Google VM machine with 24 Threads and 96 GB of ram.
As you have mentioned on the comments:
It's a Intel Xeon CPU @2.30 GHz, 12 physical core
That is the reason that when you moved to 16 thread you stop (almost) linearly scaling, because you are no longer just using physical cores but also logic cores (i.e., hyper-threading).
I expected that static would be the best since the iterations takes approximately the same time, while the dynamic would introduce too much overhead.
Most of the overhead of the dynamic distribution comes from the locking step performed by the threads to acquire the new iteration to work with. It just looks to me that there is not much thread locking contention going on, and even if it is, it is being compensated by better loading balancing achieved with the dynamic scheduler. I have seen this exact pattern before there is not wrong with it.
Aside note you can transform your code into:
I have a Docker container which I'm trying to deploy as a Heroku application. My application is called...
ANSWERAnswered 2021-May-31 at 00:47
Since you do not have a detailed log file, it is difficult to troubleshoot here. You can try doing this first to pinpoint the exact issue:
On PostgreSQL 12 with PostGIS extension, I have two tables defined as follows:...
ANSWERAnswered 2021-May-19 at 19:37
Processing records 1 by 1, in a loop, induces a lot of network traffic to the DB.
Instead, try to update all entries at once, in a single statement (which you can send from the pyton script if you wish).
I've read a bit about integrating scaling with cross-fold validation and hyperparameter tuning without risking data leaks. The most sensical solution I've found (according to my knowledge) involves creating a pipeline that includes the scalar and GridSeachCV, for when you want to grid search and cross-fold validate. I've also read that, even when using cross-fold validation, it is useful to, at the very beginning, create a hold-out test set for an additional, final evaluation of your model after hyperparameter tuning. Putting that all together looks like this:...
ANSWERAnswered 2021-May-27 at 06:18
GridSearchCV will help you find the best set of hyperparameter according to your pipeline and dataset. In order to do that it will use cross validation (split the your train dataset into 5 equal subset in you case). This means that your
best_estimator will be trained on 80% of the train set.
As you know the more data a model see, the better its result is. Therefore once you have the optimal hyperparameters, it is wise to retrain the best estimator on all your training set and assess its performance with the test set.
You can retrain the best estimator using the whole train set by specifying the parameter
refit=True of the Gridsearch and then score your model on the
best_estimator as follows:
I have a form with mandatory inputs and added a onClick event listener on the submit button to display a loading git when the program is charging. The problem is that the onClick function is triggered every time the button is clicked and I want it to be only if the form is complete and sent.
How can I put a condition in my jQuery function for that ?
Here is the HTML and JS:...
ANSWERAnswered 2021-May-27 at 05:58
You can use checkValidity() this will return true/false depending on this you can show your
Demo Code :
No vulnerabilities reported
You can use knn like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the knn component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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