def test(): # base data X = np.random.randn( 1000, 1 ) * 10 + 50 Y = X * 2 - 10 # add noise X += np.random.randn( 1000, 1 ) * 2 Y += np.random.randn( 1000, 1 ) * 2 # split trainX = X[ :900 ] trainY = Y[ :900 ] testX = X[ 900: ] testY = Y[ 900: ] # for prediction line plotX = np.array( [ min( X ), max( X ) ] ) iters = 2000 name = [ "RMSProp", "Momentum", "Nesterov", "SGD", "Rprop", "Adam" ] model = [ LLS( 1, 1, update=Update.RmsProp() ), LLS( 1, 1, update=Update.Momentum( 1e-7 ) ), LLS( 1, 1, update=Update.NesterovMomentum( 1e-7 ) ), LLS( 1, 1, update=Update.Sgd( 1e-7 ) ), LLS( 1, 1, update=Update.Rprop() ), LLS( 1, 1, update=Update.Adam() ) ] error = np.zeros( ( len( model ), iters ) ) for i in range( iters ): for m in range( len( model ) ): error[ m, i ] = model[ m ].partial_fit( trainX, trainY ) print( i + 1, "complete" ) # plot results plt.figure() plt.title( 'Data Space' ) plt.scatter( trainX, trainY, label='train' ) plt.scatter( testX, testY, label='test' ) plt.plot( plotX, model[ 4 ].predict( plotX ).x_, label='prediction' ) plt.legend() plt.figure() plt.title( 'Error Curves' ) for m in range( len( model ) ): plt.semilogy( error[ m ], label=name[ m ] ) plt.legend() plt.show()