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kandi X-RAY | KNN Summary
kandi X-RAY | KNN Summary
This is a course assignment, and we are supposed to implement the basic K-Nearest Neighbor algorithm. Below are the basic classes deisgned in the initial phase:.
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- Test program
- Computes the knn for the given dataset
- Read test file from file
- Read train records from a file
- Get the most likely label from the array of neighbors
- Outputs the predictions file
- Finds the nearest neighbors of the training record
- Extract group name from file path
- Returns the KNN distance between two records
- Returns the Euclidean distance between two records
- Returns the distance between two records
KNN Key Features
KNN Examples and Code Snippets
Community Discussions
Trending Discussions on KNN
QUESTION
I want to find unique pairs for a test group, meaning each individual in the control group should only be chosen once. I have Gender, Age, and Education available to match them. I segmented the groups for Gender and Education since they are binary categories. Afterward, I want to find the best match in Age to a certain test individual - therefore the KNN approach with 1 nearest neighbor. The dummyData I'm using is available here.
The following part is the initialization and the segmentation:
...ANSWER
Answered 2022-Apr-04 at 13:56I ended up using only age in the KNN matching (and manually matching on the binary features), doing the following solution:
QUESTION
I want to save a Tensorflow model and then later use it for deployment purposes. I dont want to use model.save()
to save it because my purpose is to somehow 'pickle' it and use it in a different system where tensorflow is not installed, like:
ANSWER
Answered 2022-Mar-16 at 07:12Using joblib
seems to work on TF
2.8 and since you have a very simple model, you can train it on Google Colab and then just use the pickled file on your other system:
QUESTION
I am currently using the scikit learn module in order to help with a crime prediction problem. I am having an issue batch coding the entire Dataframe that I have with the knn.predict method.
How can I batch code the entire two columns of my Dataframe with the knn.predict() method in order to store in another Dataframe the output?
...ANSWER
Answered 2022-Mar-08 at 03:29Let the new set to be predicted is 'knn_df_predict'. Assuming same column names,try the following lines of code :
QUESTION
I would like to train a SVM and KNN model, I was wondering if anyone could advise for training a Machine Learning model which processing is more appropriate?
...ANSWER
Answered 2022-Mar-01 at 07:19Normalization rescales the values into a range of [0,1], Standardization rescales data to have a mean (μ) of 0 and standard deviation (σ) of 1.
It's recommended to use Standardization before applying KNN, check this article for some explanation.
QUESTION
I have 3 columns namely Models(should be taken as index), Accuracy without normalization, Accuracy with normalization (zscore, minmax, maxabs, robust) and these are required to be created as:
...ANSWER
Answered 2022-Feb-20 at 13:01There's a dirty way to do this, I'll write about it till someone answers with a better idea. Here we go:
QUESTION
I want to impute missing values by KNN, and I use this method to select best K:
...ANSWER
Answered 2022-Feb-20 at 13:52There is actually one way to check best K where there is no need to split between train & test.
The method is to study the Density with different K numbers, but it is just for One variable (I will select the one with more imputations needed). The one nearest to original distribution is the best to select.
QUESTION
I wanted to use the optimal k value to conduct kNN clasification and predict the dependent variable diabetes in test set using train set and compare the results with the real values.
I've already got optimal k value and got the accruacy already. After that, I wanted to compare the results with the real value with using confussionMatrix but I got the problem with the different length.
I've already checked that nrow and length quantities are same(with 74) but it still have same problem.
Could you help me to overcome this problem?
My codes are as like below
...ANSWER
Answered 2022-Feb-11 at 05:27I think you are looking to this:
QUESTION
I just wanted to conduct a kNN classification with the situation when k is 3. I would like to predict the dependent variable “diabetes” in valid set using train set and calculate the accuracy.
But I faced to the error message with
Error in knn(train = TrainXNormDF, test = ValidXNormDF, cl = MLdata2[, : 'train' and 'class' have different lengths
I can't solve this problem with get approach with
...ANSWER
Answered 2022-Feb-10 at 14:48Your cl
variable is not the same length as your train
variable. MLValidY
only has 74 observations, while TrainXNormDF
has 224.
cl
should provide the true classification for every row in your training set.
Furthermore, cl
is a data.frame instead of a vector.
Try the following:
QUESTION
background: I have a machine learning model in which given an object returns an embedding vector with dimension d, the model is trained in a way such that the semantic similarity of two embedding vectors is very close. Now, the verification process is relatively simple, I can take something like the cosine similarity of the two vectors. For recognition, it's a little bit complicated, either I can loop through all the anchor documents and compare the cosine similarity, or use something like kNN (online).
problem: I have a list of embedding vectors, each vector has a dimension d, with length N. Each vector contains floating-point data.
What will be an efficient data structure + algorithm that can do the following:
- Can add a new vector with a unique ID to the list efficiently (<= logarithmic complexity)
- Search with a random vector in the list, and retrieve top k vectors, such that the Manhattan distance / L1 norm is minimum for those vectors efficiently (hopefully, <= logarithmic complexity).
example:
...ANSWER
Answered 2021-Dec-25 at 05:09QUESTION
- I use Python.Net for C# interaction with Python libraries. I solve the problem of text classification. I use FastText to index and get the vector, as well as Sklearn to train the classifier (Knn).During the implementation, I encountered a lot of problems, but all were solved, with the exception of one. After receiving the vectors of the texts on which I train Knn, I save them to a separate text file and then, if necessary, use it.
ANSWER
Answered 2021-Dec-10 at 21:59I solved this issue for a couple of days and each time I thought it was worth reading the documentation on python.net .
As a result, I found a solution and it turned out to be quite banal, it is necessary to represent X_vec
not as a float[]
, but as a List
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Install KNN
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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