def max_eig(A, iterations, numeric_method="qr"):
"""
Function to return the largest eigenvalue of a matrix and its corresponding eigenvector.
A must be square but need not be symmetric. Tries to first use uLab `np.linalg.eig`
that is better optimised but requires a symmetric matrix. Failing this, power iteration
algorithm is used.
"""
try:
lam, V = np.linalg.eig(A)
v = V[:, np.argmax(lam)]
except ValueError:
if numeric_method == "power_iteration":
lam, v = power_iteration(A, iterations)
else:
if numeric_method != "qr":
print("Unknown `numeric_method` arg: defaulting to QR solver")
lam, v = solve_eig_qr(A, iterations)
lam = lam[0] # only need first eigen val (largest returned first)
v = v[:, 0] # only first eig vector
return lam, v
range(250 - 5, 250)],
dtype=np.int16)
print(np.argmin(a))
print(np.argmin(a, axis=0))
print(np.argmin(a, axis=1))
a = np.array(
[range(2**56 - 3, 2**56),
range(2**16 - 3, 2**16),
range(2**8 - 3, 2**8)],
dtype=np.float)
print(np.argmin(a))
print(np.argmin(a, axis=0))
print(np.argmin(a, axis=1))
print("Testing np.argmax:")
print(np.argmax([1]))
print(np.argmax(np.array([1], dtype=np.float)))
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.uint8)
print(np.argmax(a))
print(np.argmax(a, axis=0))
print(np.argmax(a, axis=1))
a = np.array([range(255 - 5, 255),
range(240 - 5, 240),
range(250 - 5, 250)],
dtype=np.uint8)
print(np.argmax(a))
print(np.argmax(a, axis=0))
print(np.argmax(a, axis=1))
a = np.array([range(255 - 5, 255),
range(240 - 5, 240),
range(250 - 5, 250)],
while True:
i = (i + 0.5) % 256 # run from 0 to 255
TILT_COLOR = colorwheel(i)
if MODE == 0: # If currently off...
enable.value = True
power_on(POWER_ON_DURATION) # Power up!
MODE = LASTMODE
elif MODE >= 1: # If not OFF MODE...
mic.record(samples_bit, len(samples_bit))
samples = np.array(samples_bit[3:])
spectrum = spectrogram(samples)
spectrum = spectrum[:128]
spectrum[0] = 0
spectrum[1] = 0
peak_idx = np.argmax(spectrum)
peak_freq = peak_idx * 16000 / 256
# print((peak_idx, peak_freq, spectrum[peak_idx]))
magnitude = spectrum[peak_idx]
# time.sleep(1)
if peak_freq == 812.50 and magnitude > SOUND_THRESHOLD:
animations.next()
time.sleep(1)
if peak_freq == 875 and magnitude > SOUND_THRESHOLD:
if MODE == 1:
MODE = 2
print("mode = 2")
LASTMODE = 2
time.sleep(1)
elif MODE == 2:
MODE = 1
j = cycles[i]
indices[i], indices[-j] = indices[-j], indices[i]
yield tuple(pool[i] for i in indices[:r])
break
else:
return
# Combinations expected to throw
try:
print(np.argmin([]))
except ValueError:
print("ValueError")
try:
print(np.argmax([]))
except ValueError:
print("ValueError")
# Combinations expected to succeed
print(np.argmin([1]))
print(np.argmax([1]))
print(np.argmin(np.array([1])))
print(np.argmax(np.array([1])))
print()
print("max tests")
for p in permutations((100, 200, 300)):
m1 = np.argmax(p)
m2 = np.argmax(np.array(p))
print(p, m1, m2)
def standardise(X):
axis = np.argmax(X.shape)
minor_shape = np.min(X.shape)
mu = np.mean(X, axis=axis).reshape((minor_shape, 1))
sigma = np.std(X, axis=axis).reshape((minor_shape, 1))
return (X - mu) / sigma