Finding the Euclidean distance between two points is a fundamental task in various fields, such as mathematics, statistics, and machine learning. This can be particularly useful in applications such as clustering, classification, and dimensionality reduction, where distances between data points are a crucial component of the analysis. In this thread, we will explore how to find the point-to-point distances using NumPy’s array.
1. NumPy's "broadcasting":
NumPy allows performing operations on arrays with different shapes or dimensions through a process called broadcasting . This process makes arrays compatible by automatically adding dimensions or replicating elements as needed.
In the above example, NumPy’s broadcasting used to subtract each point from all other points in the array. The resulting array is then squared and summed along the axis=2. Finally, the square root of this sum is taken to obtain the Euclidean distance between each pair of points, which is stored in the distances variable.
The above method is efficient in terms of memory usage, as it does not create any unnecessary arrays or variables, and is likely to be the fast, as it uses NumPy's optimized broadcasting functionality.
2. By "scipy.spatial.distance_matrix()" function:
The `scipy.spatial.distance_matrix()` function is a method in the SciPy library that calculates the pairwise distance between all pairs of vectors in two sets of vectors. It returns a matrix of distances where the (i,j)th element is the distance between the ith vector in the first set and the jth vector in the second set.The above code uses the distance_matrix function() from Scipy's spatial module. This function calculates the Euclidean distance between all pairs of points in the array and returns a NumPy array containing the distances.
This method is likely to be slower than method 1, as it uses a function from an external library, and is memory-efficient, as it only creates a single array to store the distances.
3. Nested "loops":
You can used python nested loops to calculate distance.The above code creates a random vector of coordinates and calculates the distances between each pair of points using the Euclidean distance formula. It stores the distances in an empty array and prints the array.
This method is not efficient, as it creates an empty array to store the distances and uses nested loops to fill it and it is memory-intensive, as it creates an array of zeros to store the distances and then fills it with values.