e3c2740c3f3e2451dd1dfed82a1cdbba63139ba4,dipy/reconst/fwdti.py,,_wls_iter,#Any#Any#Any#Any#Any#Any#Any#,308

Before Change

            all_new_params = np.dot(np.dot(invWS2W, WS2), y)

            // compute F2
            S0r = np.matlib.repmat(all_new_params[:, 6], nvol, 1)
            SIpred = (1-FS)*np.exp(np.dot(W, all_new_params)) + FS*S0r*SFW
            F2 = np.sum(np.square(SI - SIpred))

            // Select params for lower F2

After Change

        df = df * 0.1
        fs = np.linspace(flow+df, fhig-df, num=ns)  // sampling f
        // repeat fw contribution for all the samples
        SFW = np.array([fwsig,]*ns)
        FS, SI = np.meshgrid(fs, sig)
        for r in range(riterations):
            // Free-water adjusted signal
            S0 = np.exp(-params[6])
            y = np.log((SI - FS*S0*SFW.T) / (1 - FS))

            // Estimate tissue"s tensor from inv(A.T*S2*A)*A.T*S2*y
            S2 = np.diag(np.square(np.dot(W, params)))
            WS2 = np.dot(W.T, S2)
            invWS2W = np.linalg.pinv(np.dot(WS2, W))
            all_new_params = np.dot(np.dot(invWS2W, WS2), y)

            // compute F2
            S0r = np.exp(-np.array([all_new_params[6],]*nvol))
            SIpred = (1-FS)*np.exp(np.dot(W, all_new_params)) + FS*S0r*SFW.T
            F2 = np.sum(np.square(SI - SIpred), axis=0)

            // Select params for lower F2

Instances