How to find similarity measure between two sentences using Simple Matching Coefficient?

Question

I have been following the code on this link to find the similarity measure between the input X and Y:

def similarity(X, Y, method):
    X = np.mat(X)
    Y = np.mat(Y)
    N1, M = np.shape(X)
    N2, M = np.shape(Y)

    method = method[:3].lower()
    if method=='smc': # SMC
        X,Y = binarize(X,Y);
        sim = ((X*Y.T)+((1-X)*(1-Y).T))/M
    return sim

def binarize(X,Y=None):
    ''' Force binary representation of the matrix, according to X>median(X) '''
    if Y==None:
        X = np.matrix(X)
        Xmedians = np.ones((np.shape(X)[0],1)) * np.median(X,0)
        Xflags = X>Xmedians
        X[Xflags] = 1; X[~Xflags] = 0
        return X
    else:
        X = np.matrix(X); Y = np.matrix(Y);
        XYmedian= np.median(np.bmat('X; Y'),0)
        Xmedians = np.ones((np.shape(X)[0],1)) * XYmedian
        Xflags = X>Xmedians
        X[Xflags] = 1; X[~Xflags] = 0
        Ymedians = np.ones((np.shape(Y)[0],1)) * XYmedian
        Yflags = Y>Ymedians
        Y[Yflags] = 1; Y[~Yflags] = 0
        return [X,Y]

However, it assumes that the input X and Y should be N1 * M and N2 * M dimensional matrices respectively. I am confused at how to convert my input that are variable length sentences into the required input format.

Also, I would be grateful if someone could suggest me some other method to find the same.

Answer 1

How about this:

import pandas as pd
df1=pd... #I'd like to see how you generate your data
df2=pd...
cols_common=list(set(df1.columns).intersection(df2.columns))
df1=df1[cols_common]
df2=df2[cols_common]
result=similarity(df1,df2,'smc')

Of course, this approach presumes that the two tables have one or more columns in common. you could also delete columns from the larger dataframe arbitrarily, but I wouldn't recommend this without knowing your use case