def make_plot(surf, random=False, append=False):
from util.plotly import Plot
mult = 5
fun = lambda x: np.cos(x[0] * mult) + np.sin(x[1] * mult)
np.random.seed(0)
p = Plot()
low = -0.1
upp = 1.1
dim = 2
plot_points = 2000
N = 30
if random:
x = np.random.random(size=(N, dim))
else:
N = int(round(N**(1 / dim)))
x = np.array([
r.flatten() for r in np.meshgrid(np.linspace(low, upp, N),
np.linspace(low, upp, N))
]).T
y = np.array([fun(v) for v in x])
# x = np.array([[0.5,0.5], [0.2,0.2], [0.2,0.8], [0.8,0.8], [0.8,0.2]])
# y = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
p.add("Training Points", *x.T, y)
surf.fit(x, y)
p.add_func("VMesh", surf, *([(low, upp)] * dim), plot_points=plot_points)
p.plot(file_name="vmesh.html", append=append)
def minimize(objective,
solution,
bounds=None,
args=tuple(),
max_time=DEFAULT_MAX_TIME_SEC,
min_steps=DEFAULT_MIN_STEPS,
max_steps=DEFAULT_MAX_STEPS,
min_improvement=DEFAULT_MIN_IMPROVEMENT,
display=False,
method=DiRect,
checkpoint=False,
checkpoint_file=CHECKPOINT_FILE):
# Convert the solution into a float array (if it's not already)
solution = np.asarray(solution, dtype=float)
# Generate some arbitrary bounds
if type(bounds) == type(None):
upper = solution + np.ones((len(solution), )) * DEFAULT_SEARCH_SIZE
lower = solution - np.ones((len(solution), )) * DEFAULT_SEARCH_SIZE
bounds = list(zip(lower, upper))
# Initialize a tracker for halting the optimization
t = Tracker(objective, max_time, min_steps, min_improvement, display,
checkpoint, checkpoint_file)
# Get the initial objective value of the provided solution.
t.check(solution, *args)
# Call the optimization function and get the best solution
method(t.check, t.done, bounds, solution, args=args)
if display:
print()
try:
from util.plotly import Plot
name = objective.__name__.title()
p = Plot(f"Minimization Performance on Objective '{name}'",
"Trial Number", "Objective Value")
trial_numbers = [n for (o, n, s) in t.record]
obj_values = [o for (o, n, s) in t.record]
p.add("",
trial_numbers,
obj_values,
color=p.color(1),
mode="lines+markers")
p.show(show_legend=False)
except:
pass
# Remove the checkpoint file
if os.path.exists(checkpoint_file): os.remove(checkpoint_file)
return t.best_sol
# print("weights: ",weights)
# print("predictions: ",predictions)
return predictions
if __name__ == "__main__":
from util.plotly import Plot
mult = 5
fun = lambda x: np.cos(x[0]*mult) + np.sin(x[1]*mult)
np.random.seed(0)
p = Plot()
low = 0
upp = 1
dim = 2
plot_points = 2000
N = 4
random = True
if random:
x = np.random.random(size=(N,dim))
else:
N = int(round(N ** (1/dim)))
x = np.array([r.flatten() for r in np.meshgrid(np.linspace(low,upp,N), np.linspace(low,upp,N))]).T
y = np.array([fun(v) for v in x])
p.add("Training Points", *x.T, y)
surf = Linn()
surf.fit(x,y)
p.add_func("VMesh", surf, *([(low,upp)]*dim), plot_points=plot_points)
p.plot(file_name="vmesh.html")
# Solve the actual least-squares problem
A = np.concatenate(([pts[:,1]*pts[:,0]**2], [pts[:,1]*pts[:,0]], [pts[:,1]])).T
B = pts[:,-1]
a,b,c = np.linalg.lstsq(A,B)[0]
print("Actual: d (%.3e x^2 + %.3e x + %.3e)"%(a,b,c))
# Solve the least squares as an optimizaiton problem
fun = lambda c: sum(
(pts[:,1]*(c[0]*pts[:,0]**2 + c[1]*pts[:,0]**1 + c[2]*pts[:,0]**0)
-pts[:,-1])**2)
a,b,c = minimize(fun, [0,0,0])
print("Approx: d (%.3e x^2 + %.3e x + %.3e)"%(a,b,c))
# Add the fit function to the plot
fit = lambda x: x[1]*(a*x[0]**2 + b*x[0] + c)
p.add("Points", *(pts.T))
p.add_func("Fit", fit,[0,10000],[2,12])
p.plot()
def ydhm(total_seconds):
minutes = total_seconds / 60
minutes, hours = minutes%60, (minutes-minutes%60) / 60
hours, days = hours%24, (hours-hours%24) / 24
days, years = days%365, (days-days%365) / 365
return years, days, hours, minutes
n = 50
d = 1000000000
print("%i dimensions:"%d)
for n in range(10,101,10):
print(("%i points: %i years, %i days, %i hours, %i "+