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Solución:
Enfoque #1: NumPy-Vectorizado
Aquí hay uno vectorizado:
def check_all(a, state): # a is input matrix/array
# Get zeros and ones all masks
zm = (a==0).all(1)
om = (a==1).all(1)
# "Attach" boundaries with False values at the start of these masks.
# These will be used to detect rising edges (as indices) on these masks.
zma = np.r_[False,zm]
oma = np.r_[False,om]
omi = np.flatnonzero(oma[:-1] < oma[1:])
zmi = np.flatnonzero(zma[:-1] < zma[1:])
# Group the indices and the signatures (values as 1s and -1s)
ai = np.r_[omi,zmi]
av = np.r_[np.ones(len(omi),dtype=int),-np.ones(len(zmi),dtype=int)]
# Sort the grouped-indices, thus we would know the positions
# of these group starts. Then index into the signatures/values
# and indices with those, giving us the information on how these signatures
# occur through the length of the input
sidx = ai.argsort()
val,aidx = av[sidx],ai[sidx]
# The identical consecutive signatures are to be removed
mask = np.r_[True,val[:-1]!=val[1:]]
v,i = val[mask],aidx[mask]
# Also, note that we are assigning all 1s as +1 signature and all 0s as -1
# So, in case the starting signature is a 0, assign a value of 0
if v[0]==-1:
v[0] = 0
# Initialize 1D o/p array, which stores the signatures as +1s and -1s.
# The bigger level idea is that performing cumsum at the end would give us the
# desired 1D output
out1d = np.zeros(len(a),dtype=a.dtype)
# Assign the values at i positions
out1d[i] = v
# Finally cumsum to get desired output
out1dc = out1d.cumsum()
# Correct the starting positions based on starting state value
out1dc[:i[0]] = state
# Convert to 2D view for mem. and perf. efficiency
out = np.broadcast_to(out1dc[:,None],a.shape)
return out
Enfoque #2: basado en Numba
Aquí hay otro basado en numba para la memoria y, por lo tanto, el rendimiento. eficiencia -
@njit(parallel=True)
def func1(zm, om, out, start_state, cur_state):
# This outputs 1D version of required output.
# Start off with the starting given state
newval = start_state
# Loop through zipped zeros-all and ones-all masks and in essence do :
# Switch between zeros and ones based on whether the other ones
# are occuring through or not, prior to the current state
for i,(z,o) in enumerate(zip(zm,om)):
if z and cur_state:
cur_state = ~cur_state
newval = 0
if o and ~cur_state:
cur_state = ~cur_state
newval = 1
out[i] = newval
return out
def check_all_numba(a, state):
# Get zeros and ones all masks
zm = (a==0).all(1)
om = (a==1).all(1)
# Decide the starting state
cur_state = zm.argmax() < om.argmax()
# Initialize 1D o/p array with given state values
out1d = np.full(len(a), fill_value=state)
func1(zm, om, out1d, state, cur_state)
# Broadcast into the 2D view for memory and perf. efficiency
return np.broadcast_to(out1d[:,None],a.shape)
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