def plot_seq_recall(pattern_list, states_as_patterns):
'''
I have set a stopping criterion which will consider one recall instance to be successful iff we recall the entire sequence.
'''
seq_length = len(pattern_list)
seq_gen = np.array([])
overlap_gen = np.array([])
for i in range(len(states_as_patterns)):
overlap_list = pattern_tools.compute_overlap_list(
states_as_patterns[i], pattern_list)
# print(overlap_list.argmax(),overlap_list.max())
seq_gen = np.append(seq_gen, overlap_list.argmax())
overlap_gen = np.append(overlap_gen, overlap_list.max())
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlim(-0.1, seq_length + 2)
ax.set_ylim(-0.1, seq_length + 2)
ax.grid(True)
seq_gen, = ax.plot(np.arange(0, seq_length + 2, 1), seq_gen, 'o')
ax.set_xlabel('State Evolution')
ax.set_ylabel('Sequence Generated')
plt.tight_layout
plt.show()
def plot_state_sequence_and_overlap(state_sequence,
pattern_list,
reference_idx,
color_map="brg",
suptitle=None):
"""
For each time point t ( = index of state_sequence), plots the sequence of states and the overlap (barplot)
between state(t) and each pattern.
Args:
state_sequence: (list(numpy.ndarray))
pattern_list: (list(numpy.ndarray))
reference_idx: (int) identifies the pattern in pattern_list for which wrong pixels are colored.
"""
if reference_idx is None:
reference_idx = 0
reference = pattern_list[reference_idx]
f, ax = plt.subplots(2, len(state_sequence))
_plot_list(ax[0, :], state_sequence, None, "S{0}", color_map)
for i in range(len(state_sequence)):
overlap_list = pattern_tools.compute_overlap_list(
state_sequence[i], pattern_list)
ax[1, i].bar(range(len(overlap_list)), overlap_list)
ax[1,
i].set_title("m = {1}".format(i,
round(overlap_list[reference_idx],
2)))
ax[1, i].set_ylim([-1, 1])
ax[1, i].get_xaxis().set_major_locator(plt.MaxNLocator(integer=True))
if i > 0: # show lables only for the first subplot
ax[1, i].set_xticklabels([])
ax[1, i].set_yticklabels([])
if suptitle is not None:
f.suptitle(suptitle)
plt.show()
def plot_all_overlaps(pattern_list, states_as_patterns, interval):
f, ax = plt.subplots(len(np.arange(interval[0], interval[1], 1)),
1,
sharex=True)
for i in np.arange(interval[0], interval[1], 1):
overlap_list = pattern_tools.compute_overlap_list(
states_as_patterns[i], pattern_list)
ax[i - interval[0]].bar(range(len(overlap_list)), overlap_list)
ax[i - interval[0]].grid(True)
ax[i - interval[0]].set_xlabel('Pattern Name')
ax[i - interval[0]].set_ylim(-1, 1)
f.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False)
plt.setp([a.get_yticklabels() for a in f.axes[:]], visible=False)
plt.tight_layout
plt.show()
def plot_max_overlap_amount(pattern_list, states_as_patterns):
seq_length = len(pattern_list)
seq_gen = np.array([])
overlap_gen = np.array([])
for i in range(len(states_as_patterns)):
overlap_list = pattern_tools.compute_overlap_list(
states_as_patterns[i], pattern_list)
# print(overlap_list.argmax(),overlap_list.max())
seq_gen = np.append(seq_gen, overlap_list.argmax())
overlap_gen = np.append(overlap_gen, overlap_list.max())
fig = plt.figure()
ax2 = fig.add_subplot(111)
ax2.bar(range(len(overlap_gen)), overlap_gen)
ax2.grid(True)
ax2.set_xlabel('State Evolution')
ax2.set_ylabel('Maximum Overlap Amount')
plt.tight_layout
plt.show()
def is_seq_generated(pattern_list,states_as_patterns): ''' Computes if sequence is generated or not ''' seq_length=len(pattern_list) seq_gen = np.array([]) overlap_gen = np.array([]) for i in range(len(states_as_patterns)): overlap_list = pattern_tools.compute_overlap_list(states_as_patterns[i], pattern_list) # print(overlap_list.argmax(),overlap_list.max()) seq_gen = np.append(seq_gen,overlap_list.argmax()) overlap_gen = np.append(overlap_gen,overlap_list.max()) # print(seq_gen) # print(overlap_gen) if np.size(np.nonzero(seq_gen[:seq_length] - np.arange(0,seq_length,1))): return 0 #not generated else : return 1 #generated