def plot_estim_quick(gamma,
gamma_values=[
0.1, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02,
0.01
],
option_save="",
path_Image="",
ImageName=""):
i = np.abs(np.array(gamma_values) - gamma).argmin()
gamma_close = round(gamma_values[i], 3)
### load
res = np.load(Current_directory + '\\Results\\res_gamma' +
str(gamma_close) + '.npy').flatten().reshape((5, -1))
### plot
index = res[0, :]
error1 = res[1, :]
error2 = res[2, :]
error3 = res[3, :]
error4 = res[4, :]
df = [[index, error1], [index, error2], [index, error3], [index, error4]]
labels = ["1/n", "step_cste", "SAGA", "PASS"]
mark = ['o', '*', 'x', 'v']
bbox_to_anchor_0 = (0.7, .75)
plt.figure(figsize=(8, 5))
pltg.Plot_plot(df,
labels,
xlabel="Number of iterations",
ylabel="Log L2 - error",
option=option_save,
path=path_Image,
ImageName=ImageName,
Nset_tick_x=False,
mark=mark,
bbox_to_anchor_0=bbox_to_anchor_0)
plt.show()
##### Plot the improvement
##### Plot v1
start = 0
option_save = ""
path_Image = Path_parent_directory + "\\Image"
ImageName = "\\improvement__optimal_execution_f_final"
df = [[error_v01[start:, 0], np.log(error_v01[start:, 1])]]
labels = [" Benchmark"]
mark = ['o']
fig = plt.figure(figsize=(8, 5))
pltg.Plot_plot(df,
labels,
xlabel="Number of iterations",
ylabel="Log L2 - error",
option=option_save,
path=path_Image,
ImageName=ImageName,
Nset_tick_x=False,
mark=mark)
###############################################################################
###############################################################################
############################# Plot the values #################################
###############################################################################
###############################################################################
##### Plot the values
option_save = ""
path_Image = Path_parent_directory + "\\Image"
ImageName = "\\improvement__optimal_execution_theo"
