function = numpy.poly1d([0.16, -1.57, 4.38, -1.15, -6.29, 0.15]) print(f"Patikrintos x reikšmės {function.roots}") # function = b_part # function = resistance_task # test = function(-0.0001) # print(test) derivative = function.deriv() # derivative = b_part_derivative # derivative = None alpha = -10 max_iterations = 1000 precision = 1e20 eps = 1e-4 window = Window(title='Skaitmeniniai algoritmai 12 vr.', size='1250x630') # function_x_values = numpy.arange(-2, 1, 0.0001) # function_x_values = numpy.arange(-1, 1, 0.001) # g(x) function_x_values = numpy.arange(-3.504, 28.375, 0.001) # f(x) function_y_values = [function(x) for x in function_x_values] graph = Graph(start_x=function_x_values[0], end_x=function_x_values[-1], frame=window.get_graph_frame()) graph.add_toolbar() # graph.show_function(function_x_values, function_y_values, 'b', 'f(x)') methods = NumericalMethod(precision, eps, alpha, max_iterations, function, derivative, function_x_values, graph) window.add_buttons(methods) window.start()
# function = resistance_task # test = function(-0.0001) # print(test) derivative = function.deriv() # derivative = b_part_derivative # derivative = None alpha = -10 max_iterations = 1000 precision = 1e20 eps = 1e-4 window = Window(title='Skaitmeniniai algoritmai 12 vr.', size='1250x630') # function_x_values = numpy.arange(-2, 1, 0.0001) # function_x_values = numpy.arange(-1, 1, 0.001) # g(x) function_x_values = numpy.arange(-3.504, 28.375, 0.001) # f(x) function_y_values = [function(x) for x in function_x_values] graph = Graph(start_x=function_x_values[0], end_x=function_x_values[-1], frame=window.get_graph_frame()) graph.add_toolbar() # graph.show_function(function_x_values, function_y_values, 'b', 'f(x)') methods = NumericalMethod(precision, eps, alpha, max_iterations, function, derivative, function_x_values, graph) window.add_buttons(methods) window.start()