I want to model Euclidean distance using MIT-BIH ECG

Question

I am not good at Python. I have a csv file, and I want to calculate cosine simularity or Euclidian Distance using them. But, I don't know how to use the csv file, so please let me know the documents or materials I can use.

Answer 1

You can find euclidean distance using scipy.spatial.distance.euclidean or numpy.linalg.norm. Your problem statement can be broken down as follows:

Steps

1. Load data from a CSV
2. Calculate euclidean distance

Solution

You would need these libraries.

import numpy as np
import pandas as pd
from scipy.spatial.distance import euclidean

Step-1

Load CSV. We assume here that your CSV file has 2 or more columns and the target columns names are x and y.

df = pd.read_csv("filename.csv")
x = df['x']
y = df['y']

Step-2

I will show given you have two arrays of data x and y how you could calculate euclidean distance.

Make Data (if necessary)

# If you do not have data, make it
x = np.arange(10)
y = np.arange(10,20)

Calculate Euclidean Distance

# using scipy
scipy_d = euclidean(x,y)

# using numpy
numpy_d = np.linalg.norm(x-y)

References

I would encourage you to see the following.

1. How can the Euclidean distance be calculated with NumPy?
2. scipy.spatial.distance.euclidean
3. pandas.read_csv