I have a df with growth variables and often some initial values are 0, in which case it produces infinite values when the value moves from zero to non-zeros.
I have a set of data and I want to compare which line describes it best (polynomials of different orders, exponential or logarithmic).
I’d like to choose the best algorithm for future. I found some solutions, but I didn’t understand which R-Squared value is correct.
I can’t seem to find any python libraries that do multiple regression. The only things I find only do simple regression. I need to regress my dependent variable (y) against several independent variables (x1, x2, x3, etc.).
def gradient(X_norm,y,theta,alpha,m,n,num_it): temp=np.array(np.zeros_like(theta,float)) for i in range(0,num_it): h=np.dot(X_norm,theta) #temp[j]=theta[j]-(alpha/m)*( np.sum( (h-y)*X_norm[:,j][np.newaxis,:] ) ) temp[0]=theta[0]-(alpha/m)*(np.sum(h-y)) temp[1]=theta[1]-(alpha/m)*(np.sum((h-y)*X_norm[:,1])) theta=temp return theta X_norm,mean,std=featureScale(X) #length of X (number of rows) m=len(X) X_norm=np.array([np.ones(m),X_norm]) n,m=np.shape(X_norm) num_it=1500 alpha=0.01 theta=np.zeros(n,float)[:,np.newaxis] X_norm=X_norm.transpose() theta=gradient(X_norm,y,theta,alpha,m,n,num_it) print theta My theta from the above code is 100.2 100.2, but it should be 100.2 61.09 in matlab which is correct. … Read more