def write_comparison_csv(file_name: str,
data,
model: Model,
parameters: Union[list, dict],
model_dt: float,
headers: Optional[List[str]] = None):
if isinstance(parameters, dict):
parameters = [parameters]
x0, y0 = find_t0_data_point(data)
t_max = find_max_t_data_point(data)[0]
x_data = [[t, xt] for t, xt, yt in data]
y_data = [[t, yt] for t, xt, yt in data]
x_datas = []
y_datas = []
for parameters0 in parameters:
model_data = model.get_data(parameters0, x0, y0, 0, t_max,
math.ceil(t_max / model_dt + 1))
x_datas.append([[t, xt] for t, xt, yt in model_data])
y_datas.append([[t, yt] for t, xt, yt in model_data])
final_data = x_data
for model_x_data in x_datas:
final_data = unite_data(final_data, model_x_data)
final_data = unite_data(final_data, y_data)
for model_y_data in y_datas:
final_data = unite_data(final_data, model_y_data)
new_headers = None if headers is None else [""] + [
"x " + h for h in headers
] + ["y " + h for h in headers]
export_array_array_to_csv(final_data, file_name, headers=new_headers)
# write some files that are used to create graphs to visually compare the model
x0, y0 = find_t0_data_point(datas[i])
t_max = find_max_t_data_point(datas[i])[0]
model_data1 = model.get_data(new_parameters1, x0, y0, 0, t_max, math.ceil(t_max/model_graph_dt+1))
model_data2 = model.get_data(new_parameters2, x0, y0, 0, t_max, math.ceil(t_max/model_graph_dt+1))
model_prey_data1 = [data_point[:2] for data_point in model_data1]
model_prey_data2 = [data_point[:2] for data_point in model_data2]
model_predator_data1 = [[data_point[0], data_point[2]] for data_point in model_data1]
model_predator_data2 = [[data_point[0], data_point[2]] for data_point in model_data2]
# write prey graph
prey_graph_data = [data_point[:2] for data_point in datas[i]]
prey_graph_data = unite_data(prey_graph_data, model_prey_data1)
prey_graph_data = unite_data(prey_graph_data, model_prey_data2)
export_array_array_to_csv(prey_graph_data, output_folder + "/" + single_data_set_prey_graph_format.format(i+1))
# write predator graph
predator_graph_data = [[data_point[0], data_point[2]] for data_point in datas[i]]
predator_graph_data = unite_data(predator_graph_data, model_predator_data1)
predator_graph_data = unite_data(predator_graph_data, model_predator_data2)
export_array_array_to_csv(predator_graph_data,
output_folder + "/" + single_data_set_predator_graph_format.format(i+1))
# write the error graph file
single_data_errors_data = [[i] + [single_data_errors[j][i] for j in range(2*len(data_file_names))]
for i in range(single_data_set_num_steps+1)]
export_array_array_to_csv(single_data_errors_data, output_folder + "/" + single_data_set_errors_out)
# run both the normal and modified gradient descent algorithms on all datasets together
new_parameters1 = find_minimal_parameters(datas, model, initial_parameters, epsilon1, step_size1,