Create optimizers.py

Author: mmarouen

optimizers.py

@@ -0,0 +1,32 @@
+#performs newton-raphson optimization
+def NGD(X,resp,Ker,classifier,lambd,tol,epochs):
+    import numpy as np
+    N,K=resp.shape
+    sv=np.repeat(True,N)
+    betahat=np.array(np.repeat(0,N+1),dtype=float)
+    intercept=0
+    t=0
+    if (classifier=="SVM") & (K==1) :
+        sv_new=np.random.choice([True,False],N,replace=True)
+        while (t<10) & np.any(sv_new != sv):
+            t+=1
+            if t>1: sv=sv_new
+            betahat=np.array(np.repeat(0,N),dtype=float)
+            intercept=0
+            Ksv=Ker[sv,:]
+            Ksv=Ksv[:,sv]
+            Ysv=np.insert(resp[sv],0,0).T
+            N1=sum(sv)
+            IntMat=Ksv+lambd*np.diag(np.ones(N1))
+            IntMat=np.hstack((np.ones(N1).reshape(N1,1),IntMat))
+            IntMat=np.vstack((np.ones(N1+1).reshape(1,N1+1),IntMat))
+            IntMat[0,0]=0
+            bsv=np.dot(np.linalg.inv(IntMat),Ysv)
+            intercept=bsv[0]
+            betahat[sv]=bsv[1:]
+            yhat=np.dot(Ker,betahat)+intercept
+            yhat=np.squeeze(np.asarray(yhat))
+            sv_new=np.squeeze(np.asarray(resp))*yhat<1
+            betahat=np.insert(betahat,0,intercept)
+            
+    return {'betahat':betahat,'n_iter':t,'sv':sv_new}