1679033100
FLANN: Fast Library for Approximate Nearest Neighbors
FLANN - Fast Library for Approximate Nearest Neighbors
FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset. FLANN is written in C++ and contains bindings for the following languages: C, MATLAB, Python, and Ruby.
Documentation
Check FLANN web page here.
Documentation on how to use the library can be found in the doc/manual.pdf file included in the release archives.
More information and experimental results can be found in the following paper:
- Marius Muja and David G. Lowe, "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration", in International Conference on Computer Vision Theory and Applications (VISAPP'09), 2009 (PDF) (BibTex)
Getting FLANN
If you want to try out the latest changes or contribute to FLANN, then it's recommended that you checkout the git source repository: git clone git://github.com/mariusmuja/flann.git
If you just want to browse the repository, you can do so by going here.
Bug reporting
Please report bugs or feature requests using github's issue tracker.
Download Details:
Author: flann-lib
Source Code: https://github.com/flann-lib/flann
License: View license
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1679033100
FLANN: Fast Library for Approximate Nearest Neighbors
FLANN - Fast Library for Approximate Nearest Neighbors
FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset. FLANN is written in C++ and contains bindings for the following languages: C, MATLAB, Python, and Ruby.
Documentation
Check FLANN web page here.
Documentation on how to use the library can be found in the doc/manual.pdf file included in the release archives.
More information and experimental results can be found in the following paper:
- Marius Muja and David G. Lowe, "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration", in International Conference on Computer Vision Theory and Applications (VISAPP'09), 2009 (PDF) (BibTex)
Getting FLANN
If you want to try out the latest changes or contribute to FLANN, then it's recommended that you checkout the git source repository: git clone git://github.com/mariusmuja/flann.git
If you just want to browse the repository, you can do so by going here.
Bug reporting
Please report bugs or feature requests using github's issue tracker.
Download Details:
Author: flann-lib
Source Code: https://github.com/flann-lib/flann
License: View license
1612595276
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1625629740
What is a Library? Using Libraries in Code Tutorial | C Library Examples
In this tutorial, we’ll be talking about what a library is and how they are useful. We will be looking at some examples in C, including the C Standard I/O Library and the C Standard Math Library, but these concepts can be applied to many different languages. Thank you for watching and happy coding!
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1677816720
A Julia Wrapper for Fast Library For Approximate Nearest Neighbors
FLANN.jl
A simple wrapper for FLANN, Fast Library for Approximate Nearest Neighbors. It has an interface similar to the NearestNeighbors package API.
Installation
Prerequisites for building binary dependency: gcc, cmake, liblz4.
Use the package manager to install:
pkg> add FLANN
Usage Example
using Distances
using FLANN
X = readdlm(Pkg.dir("FLANN", "test", "iris.csv"), ',')
v = X[:, 84]
k = 3
r = 10.0
idxs, dsts = knn(X, v, k, FLANNParameters())
# or
t = flann(X, FLANNParameters(), Minkowski(3))
inds, dists = knn(t, v, k)
# or
idxs, dsts = inrange(t, v, r)
# Do not forget to close index!
close(t)
TODO
- Documentation
Download Details:
Author: Wildart
Source Code: https://github.com/wildart/FLANN.jl
License: View license
1593571140
K-Nearest Neighbors
A perfect opening line I must say for presenting the K-Nearest Neighbors. Yes, that’s how simple the concept behind KNN is. It just classifies a data point based on its few nearest neighbors. How many neighbors? That is what we decide.
Looks like you already know a lot of there is to know about this simple model. Let’s dive in to have a much closer look.
Before moving on, it’s important to know that KNN can be used for both classification and regression problems. We will first understand how it works for a classification problem, thereby making it easier to visualize regression.
KNN Classifier
The data we are going to use is the Breast Cancer Wisconsin(Diagnostic) Data Set_. _There are 30 attributes that correspond to the real-valued features computed for a cell nucleus under consideration. A total of 569 such samples are present in this data, out of which 357 are classified as ‘benign’ (harmless) and the rest 212 are classified as _‘malignant’ _(harmful).
The diagnosis column contains ‘M’ or ‘B’ values for malignant and benign cancers respectively. I have changed these values to 1 and 0 respectively, for better analysis.
Also, for the sake of this post, I will only use two attributes from the data → ‘mean radius’ and ‘mean texture’. This will later help us visualize the decision boundaries drawn by KNN. Here’s how the final data looks like (after shuffling):
Let’s code the KNN:
# Defining X and y
X = data.drop('diagnosis',axis=1)
y = data.diagnosis
# Splitting data into train and test
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=42)
# Importing and fitting KNN classifier for k=3
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train,y_train)
# Predicting results using Test data set
pred = knn.predict(X_test)
from sklearn.metrics import accuracy_score
accuracy_score(pred,y_test)
The above code should give you the following output with a slight variation.
0.8601398601398601
What just happened? When we trained the KNN on training data, it took the following steps for each data sample:
- Calculate the distance between the data sample and every other sample with the help of a method such as Euclidean.
- Sort these values of distances in ascending order.
- Choose the top K values from the sorted distances.
- Assign the class to the sample based on the most frequent class in the above K values.
Let’s visualize how KNN drew a decision boundary on the train data set and how the same boundary is then used to classify the test data set.
KNN Classification at K=3. Image by Sangeet Aggarwal
With the training accuracy of 93% and the test accuracy of 86%, our model might have shown overfitting here. Why so?
When the value of K or the number of neighbors is too low, the model picks only the values that are closest to the data sample, thus forming a very complex decision boundary as shown above. Such a model fails to generalize well on the test data set, thereby showing poor results.
The problem can be solved by tuning the value of _n_neighbors _parameter. As we increase the number of neighbors, the model starts to generalize well, but increasing the value too much would again drop the performance.
Therefore, it’s important to find an optimal value of K, such that the model is able to classify well on the test data set. Let’s observe the train and test accuracies as we increase the number of neighbors.
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