def is_undetermined(x):
num_samples, ndim = x.shape
if num_samples <= ndim:
err.warn('regression is underdetermined: {}'.format(
x.shape)) #: {}'.format(x))
return True
else:
return False
def overapprox_x0(num_dims, prop, pwa_trace, solver=gopts.opt_engine):
cons, Vars, vars_grouped_by_state = pwatrace2cons(pwa_trace, num_dims,
prop)
cons = list(cons)
num_opt_vars = len(Vars)
nvars = num_dims.x + num_dims.pi
#directions = [(1, 0), (-1, 0), (0, 1), (0, -1)]
I = np.eye(nvars)
directions = np.vstack((I, -I))
left_over_vars = num_opt_vars - nvars
directions_ext = np.pad(directions, [(0, 0), (0, left_over_vars)],
'constant')
var_array = np.array(Vars)
for direction in directions_ext:
print(np.dot(direction, var_array))
#lambdafied = tuple(
# sym.lambdify(Vars, np.dot(direction, var_array), str('numpy')) for direction in directions_ext)
#obj_f = tuple(lambda x, l=l: l(*x) for l in lambdafied)
objs = tuple(np.dot(direction, var_array) for direction in directions_ext)
x_arr = np.array(sym.symbols(['x{}'.format(i) for i in range(nvars)]))
res = solve_mult_opt(nlpfun(solver), objs, cons, Vars)
l = len(res)
assert (l % 2 == 0)
min_res, max_res = res[:l // 2], res[l // 2:]
ranges = []
for di, rmin, rmax in zip(directions, min_res, max_res):
if (rmin.success and rmax.success):
print('{} \in [{}, {}]'.format(np.dot(di, x_arr), rmin.fun,
-rmax.fun))
ranges.append([rmin.fun, -rmax.fun])
else:
if settings.debug:
print('LP failed')
print('rmin status:', rmin.status)
print('rmax status:', rmax.status)
return None
r = np.asarray(ranges)
# due to numerical errors, the interval can be malformed
try:
ret_val = IntervalCons(r[:, 0], r[:, 1])
except ValueError:
err.warn('linprog fp failure: Malformed Interval! Please fix.')
return None
print(ret_val)
U.pause()
return ret_val
def plot_rect(self, r, edgecolor="k"):
if len(r[0]) > 2:
err.warn("dim>2, projecting before plottting the rectangle on the first 2 dims.")
c = r[0][0:2]
rng = r[1][0:2]
else:
c = r[0]
rng = r[1]
self.ax.add_patch(
patches.Rectangle(c, *rng, fill=False, edgecolor=edgecolor)
# patches.Rectangle((0.1, 0.1), 0.5, 0.5, fill=False)
)
def cell_affine_models(cell, step_sim, ntrain, ntest, tol, include_err):
"""cell_affine_models
Parameters
----------
cell : cell
step_sim : 1 time step (delta_t) simulator
tol : each abs state is split further into num_splits cells
in order to meet: modeling error < tol (module ntests samples)
Returns
-------
pwa.SubModel()
Notes
------
"""
# XXX: Generate different samples for each time step or reuse?
# Not clear!
sub_models = []
X, Y = getxy_ignoramous(cell, ntrain, step_sim)
rm = RegressionModel(X, Y)
X, Y = getxy_ignoramous(cell, ntest, step_sim)
e_pc = rm.error_pc(X, Y) # error %
if __debug__:
print('error%:', e_pc)
#error = np.linalg.norm(e_pc, 2)
# error exceeds tol in error_dims
error_dims = np.arange(len(e_pc))[np.where(e_pc >= tol)]
if len(error_dims) > 0:
err.warn('splitting on e%:{}, |e%|:{}'.format(
e_pc, np.linalg.norm(e_pc, 2)))
for split_cell in cell.split(axes=error_dims):
sub_models_ = cell_affine_models(
split_cell, step_sim, ntrain, ntest, tol, include_err)
sub_models.extend(sub_models_)
return sub_models
else:
#print('error%:', rm.error_pc(X, Y))
A, b = rm.Ab
C, d = cell.ival_constraints.poly()
e = rm.error(X, Y) if include_err else None
dmap = pwa.DiscreteAffineMap(A, b, e)
part = pwa.Partition(C, d, cell)
sub_model = pwa.SubModel(part, dmap)
if __debug__:
print('----------------Finalized------------------')
return [sub_model]
def verify_tree_ref(self):
node = self.root
# The parent of the root node should always be root
if node.p is not node:
err.warn("Root's (" + str(node) + ") parent isn't root")
return False
# Verify the parent and children pointers are valid for each subtree
left = self.verify_tree_ref_helper(node, node.l, True)
right = self.verify_tree_ref_helper(node, node.r, False)
err.log("Left of Root: " + str(left))
err.log("Left of Root: " + str(right))
return left and right
def plot_rect(self, r, edgecolor='k'):
if len(r[0]) > 2:
err.warn(
'dim>2, projecting before plottting the rectangle on the first 2 dims.'
)
c = r[0][0:2]
rng = r[1][0:2]
else:
c = r[0]
rng = r[1]
self.ax().add_patch(
patches.Rectangle(c,
*rng,
fill=False,
edgecolor=edgecolor,
linewidth=2)
#patches.Rectangle((0.1, 0.1), 0.5, 0.5, fill=False)
)
def wraped_call(tcd):
err.warn('wraped_call being used!')
tcd.input_array = np.array(
[int(round(i * cf)) for i in tcd.input_array])
tcd.state_array = np.array(
[int(round(i * cf)) for i in tcd.state_array])
tcd.x_array = np.array([int(round(i * cf)) for i in tcd.x_array])
# tcd.state_array = (tcd.state_array * cf).astype(int)
# tcd.x_array = (tcd.x_array * cf).astype(int)
fcd = controller_call_fun(tcd)
# WHY ROUND here???
# fcd.state_array = list((np.round(fcd.state_array / cf).astype(float)))
# fcd.output_array = list((np.round(fcd.output_array / cf).astype(float)))
fcd.state_array = list(fcd.state_array.astype(float) / cf)
fcd.output_array = list(fcd.output_array.astype(float) / cf)
return fcd
def trace_generator(self, depth):
# TODO: make the graph search depth bounded?
err.warn('ignoring depth')
path_gen = self.pwa_model.get_all_path_generator(
self.sources, self.targets)
path_ctr = 0
for path in path_gen:
path_ctr += 1
ptrace = [self.pwa_model.node_p(qi) for qi in path]
mtrace = [
self.pwa_model.edge_m((qi, qj)) for qi, qj in U.pairwise(path)
]
pwa_trace = PWATRACE(partitions=ptrace, models=mtrace)
x_array = azp.feasible(self.num_dims, self.prop, pwa_trace)
if x_array is not None:
concrete_trace = ConcreteTrace(x_array, pwa_trace)
print('Model Found')
yield concrete_trace, pwa_trace
print('Total paths checked: {}'.format(path_ctr))
return
def build_pwa_dt_model(AA, abs_states, sp, sys_sim):
"""build_pwa_dt_model
Parameters
----------
AA :
AA is
abs_states :
abs_states is
sp :
sp is
sys_sim :
sys_sim is
Returns
-------
Notes
------
Builds a model with time as a discrete variable.
i.e., models the behaviors resulting from several chosen time
steps and not only the one specified in .tst as delta_t.
"""
dt_steps = [0.01, 0.1, AA.plant_abs.delta_t]
err.warn('using time steps: {}'.format(dt_steps))
step_sims = [simsys.get_step_simulator(sp.controller_sim, sp.plant_sim, dt)
for dt in dt_steps]
pwa_models = {}
for dt, step_sim in zip(dt_steps, step_sims):
pwa_model = pwa.PWA()
for abs_state in abs_states:
sub_model = affine_model(abs_state, AA, sp, step_sim)
pwa_model.add(sub_model)
pwa_models[dt] = pwa_model
return pwa_models
def getxy_rel_ignoramous(cell1, cell2, force, N, sim, t0=0):
"""getxy_rel_ignoramous
Parameters
----------
force : force to return non-zero samples. Will loop for infinitiy
if none exists.
"""
xl = []
yl = []
sat_count = 0
iter_count = itertools.count()
print(cell1.ival_constraints)
print(cell2.ival_constraints)
if __debug__:
obs_cells = set()
while next(iter_count) <= MAX_ITER:
x_array, y_array = getxy_ignoramous(cell1, N, sim, t0=0)
if __debug__:
for i in y_array:
obs_cells.add(CM.cell_from_concrete(i, cell1.eps))
print('reachable cells:', obs_cells)
# satisfying indexes
sat_array = cell2.ival_constraints.sat(y_array)
sat_count += np.sum(sat_array)
xl.append(x_array[sat_array])
yl.append(y_array[sat_array])
if sat_count >= MIN_TRAIN:
break
# If no sample is found and force is True, must keep sampling till
# satisfying samples are found
if __debug__:
if sat_count < MIN_TRAIN:
err.warn('Fewer than MIN_TRAIN samples found!')
print('found samples: ', sat_count)
return np.vstack(xl), np.vstack(yl)
elif algarg in {'avl', 'avltree'}:
algo = algs[3]
elif algarg in {'wavl', 'wavltree', 'weakavl', 'weakavltree'}:
algo = algs[4]
elif algarg in {'tango', 'tangotree'}:
algo = algs[5]
elif algarg in {
'static', 'osbst', 'optimalstatic', 'opt', 'optbst', 'optimalstaticbst'
}:
algo = algs[6]
else:
err.err("Algorithm not recognized.")
debug = args.debug_level
if args.debug and debug == 0:
debug = 1
if debug == 1:
err.warn("Debug mode is set to: SIMPLE")
elif debug == 2:
err.warn("Debug mode is set to: VERBOSE")
elif debug == 3:
err.warn("Debug mode is set to: VERIFY")
ops.exec_ops(algo, args.pages, args.graphs, args.clean_off, debug)
def plot(self, X, Y, tol, title='unknown'):
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
dimX = X.shape[1]
assert (dimX == 2)
# if 1 or less rows
if X.shape[0] <= 1:
err.warn('cant plot, only 1 value!!')
return
div = 50
X_min, X_max = np.min(X, axis=0), np.max(X, axis=0)
step0 = (X_max[0] - X_min[0]) / div
step1 = (X_max[1] - X_min[1]) / div
step = min(step0, step1)
#print('x0 range:', X_min[0], X_max[0])
#print('x1 range:', X_min[1], X_max[1])
# Predict data of estimated models
xx1, xx2 = np.mgrid[X_min[0]:X_max[0]:step, X_min[1]:X_max[1]:step]
xx = np.vstack([xx1.ravel(), xx2.ravel()]).T
yy = self.predict(xx)
yy0 = yy[:, 0]
yy1 = yy[:, 1]
Y0 = Y[:, 0]
Y1 = Y[:, 1]
error_pc = self.error_pc(X, Y)
outlier0_idx = error_pc[:, 0] > tol
outlier1_idx = error_pc[:, 1] > tol
if any(outlier0_idx):
#print('X:\n', X[outlier0_idx, :])
#print('Y0_pred:', self.predict(X[outlier0_idx, :])[:, 0])
#print('Y0_true', Y0[outlier0_idx])
Y0_pred_ = self.predict(X[outlier0_idx, :])[:, 0]
Y0_pred = np.reshape(Y0_pred_, (Y0_pred_.size, 1))
# make it 2-dim to match dim of X and Y0_pred
Y0_true_ = Y0[outlier0_idx]
Y0_true = np.reshape(Y0_true_, (Y0_true_.size, 1))
epc0_ = error_pc[outlier0_idx, 0]
epc0 = np.reshape(epc0_, (epc0_.size, 1))
logger.debug('X - Y0_pred - Y0_true - error_pc')
logger.debug(
np.hstack((X[outlier0_idx, :], Y0_pred, Y0_true, epc0)))
if any(outlier1_idx):
Y1_pred_ = self.predict(X[outlier1_idx, :])[:, 1]
Y1_pred = np.reshape(Y1_pred_, (Y1_pred_.size, 1))
# make it 2-dim to match dim of X and Y1_pred
Y1_true_ = Y1[outlier1_idx]
Y1_true = np.reshape(Y1_true_, (Y1_true_.size, 1))
epc1_ = error_pc[outlier1_idx, 1]
epc1 = np.reshape(epc1_, (epc1_.size, 1))
logger.debug('X - Y1_pred - Y1_true - error_pc')
logger.debug(
np.hstack((X[outlier1_idx, :], Y1_pred, Y1_true, epc1)))
# plot the surface
#print(xx)
#print(xx2)
#print(yy[:, 0])
#embed()
################
# First subplot
################
fig = plt.figure() #figsize=plt.figaspect(2.))
fig.suptitle(title)
ax = fig.add_subplot(2, 1, 1, projection='3d')
ax.plot_surface(xx1, xx2, np.reshape(yy0, (xx1.shape)))
ax.plot(X[:, 0], X[:, 1], Y0, 'y.')
ax.plot(X[outlier0_idx, 0], X[outlier0_idx, 1], Y0[outlier0_idx], 'r.')
ax.set_title('y0 vs x')
ax.set_xlabel('x0')
ax.set_ylabel('x1')
ax.set_zlabel('y0')
#################
# Second subplot
#################
ax = fig.add_subplot(2, 1, 2, projection='3d')
ax.plot_surface(xx1, xx2, np.reshape(yy1, (xx1.shape)))
ax.plot(X[:, 0], X[:, 1], Y1, 'y.')
ax.plot(X[outlier1_idx, 0], X[outlier1_idx, 1], Y1[outlier1_idx], 'r.')
ax.set_title('y1 vs x')
ax.set_xlabel('x0')
ax.set_ylabel('x1')
ax.set_zlabel('y1')
def cell_rel_affine_models(cell1, cell2, force, step_sim, ntrain, ntest, tol, include_err):
"""cell_affine_models
Parameters
----------
cell1 : source cell
cell2 : target cell
step_sim : 1 time step (delta_t) simulator
tol : each abs state is split further into num_splits cells
in order to meet: modeling error < tol (module ntests samples)
Returns
-------
pwa.SubModel()
Notes
------
"""
# XXX: Generate different samples for each time step or reuse?
# Not clear!
sub_models = []
X, Y = getxy_rel_ignoramous(cell1, cell2, force, ntrain, step_sim)
# No samples found => no model
training_samples_found = len(X) != 0
if not training_samples_found:
return [None]
rm = RegressionModel(X, Y)
X, Y = getxy_rel_ignoramous(cell1, cell2, True, ntest, step_sim)
testing_samples_found = len(X) != 0
# If valid samples are found, compute e_pc (error %) and dims
# where error % >= given tol
if testing_samples_found:
e_pc = rm.error_pc(X, Y)
error_dims = np.arange(len(e_pc))[np.where(e_pc >= tol)]
# Otherwise, forget it!
else:
e_pc = None
error_dims = []
if __debug__:
print('error%:', e_pc)
if len(error_dims) > 0:
err.warn('splitting on e%:{}, |e%|:{}'.format(
e_pc, np.linalg.norm(e_pc, 2)))
for split_cell1 in cell1.split(axes=error_dims):
sub_models_ = cell_rel_affine_models(
split_cell1, cell2, False, step_sim, ntrain, ntest, tol, include_err)
sub_models.extend(sub_models_)
return sub_models
else:
A, b = rm.Ab
C1, d1 = cell1.ival_constraints.poly()
C2, d2 = cell2.ival_constraints.poly()
e = rm.error(X, Y) if (include_err and testing_samples_found) else None
dmap = rel.DiscreteAffineMap(A, b, e)
part1 = rel.Partition(C1, d1, cell1)
part2 = rel.Partition(C2, d2, cell2)
sub_model = rel.Relation(part1, part2, dmap)
if __debug__:
print('----------------Finalized------------------')
return [sub_model]
def warn_if_small_data_set(x):
if len(x) <= 2:
err.warn_severe('Less than 2 training samples!: {}'.format(x))
elif len(x) <= 10:
err.warn('Less than 10 training samples!: {}'.format(x))
def get_all_path_generator(
G,
source_list,
sink_list,
max_depth,
max_paths
):
# Define super source and sink nodes
# A Super source node has a directed edge to each source node in the
# source_list
# Similarily, a Super sink node has a directed edge from each sink node
# in the sink_list
dummy_super_source_node = 'source'
dummy_super_sink_node = 'sink'
num_source_nodes = len(source_list)
num_sink_nodes = len(sink_list)
# increment max_depth by 2 to accommodate edges from 'super source' and
# to 'super sink'
max_depth += 2
# Add edges:
# \forall source \in source_list. super source node -> source
edge_list = list(zip([dummy_super_source_node] * num_source_nodes,
source_list))
G.add_edges_from(edge_list)
if settings.debug:
U.eprint('source -> list')
for e in edge_list:
U.eprint(e)
# Add edges:
# \forall sink \in sink_list. sink -> super sink node
edge_list = list(zip(sink_list, [dummy_super_sink_node] *
num_sink_nodes))
G.add_edges_from(edge_list)
if settings.debug:
U.eprint('sink -> list')
for e in edge_list:
U.eprint(e)
if settings.debug:
#print(the graph first)
U.eprint('Printing graph...')
for e in G.G.edges():
s, t = e
U.eprint('{}, {}'.format(G.v_attr[s], G.v_attr[t]))
U.eprint('Printing graph...done')
path_it = G.all_shortest_paths(dummy_super_source_node,
dummy_super_sink_node,
)
if settings.debug:
U.eprint('path list')
paths = list(path_it)
for path in paths[0:max_paths]:
p = [G.v_attr[v] for v in path]
U.eprint(p)
path_it = (i for i in paths)
#############################################################
# TODO: Remove the extra step to count paths
# It is there just as a debug print
paths = list(U.bounded_iter(path_it, max_paths))
num_paths = len(paths)
err.warn('counting paths...found: {}'.format(num_paths))
def path_gen():
for path in paths:
p = [G.v_attr[v] for v in path]
yield p[1:-1]
# END: CODE TO BE REMOVED
# CORRECT CODE BELOW
# def path_gen():
# for path in U.bounded_iter(path_it, max_paths):
# p = [G.v_attr[v] for v in path]
# yield p[1:-1]
#############################################################
return path_gen()
def init(
A,
init_cons_list_plant,
final_cons,
init_d,
controller_init_state,
):
PA = A.plant_abs
d, pvt, n = init_d, (0, ), 0
plant_initial_state_set = set()
def f(init_cons_): return PA.get_abs_state_set_from_ival_constraints(init_cons_, n, d, pvt)
for init_cons in init_cons_list_plant:
init_abs_states = f(init_cons)
# filters away zero measure sets
def fnzm(as_):
intsec = PA.get_ival_cons_abs_state(as_) & init_cons
return (intsec is not None) and not intsec.zero_measure
err.warn('what is going on?')
#filt_init_abs_states = filter(fnzm, init_abs_states)
filt_init_abs_states = init_abs_states
plant_initial_state_set.update(filt_init_abs_states)
# Old code to compute plant_initial_state_set
# plant_initial_state_list = []
# for init_cons in init_cons_list_plant:
# plant_initial_state_list += \
# PA.get_abs_state_set_from_ival_constraints(init_cons, n, d, pvt)
# plant_initial_state_set = set(plant_initial_state_list)
# The below can be very very expensive in time and memory for large final
# sets!
# plant_final_state_set = \
# set(PA.get_abs_state_set_from_ival_constraints(final_cons, 0, 0, 0))
# ##!!##logger.debug('{0}initial plant states{0}\n{1}'.format('=' * 10, plant_initial_state_set))
# set control states for initial states
# TODO: ideally the initial control states should be supplied by the
# user and the below initialization should be agnostic to the type
initial_state_list = []
for plant_init_state in plant_initial_state_set:
initial_state_list.append(AA.TopLevelAbs.get_abs_state(plant_state=plant_init_state))
#final_state_list = []
# for plant_final_state in plant_final_state_set:
# final_state_list.append(AA.TopLevelAbs.get_abs_state(
# plant_state=plant_final_state,
# controller_state=controller_init_abs_state))
# print('='*80)
# print('all final states')
# for ps in plant_final_state_set:
# print(ps)
# print('='*80)
def is_final(_A, abs_state):
#print('----------isfinal-----------')
#print(abs_state.plant_state.cell_id == ps.cell_id)
#print(abs_state.plant_state in plant_final_state_set)
#print(hash(abs_state.plant_state), hash(ps))
#print(abs_state.plant_state == ps)
#if abs_state.plant_state.cell_id == ps.cell_id:
#exit()
# return abs_state.plant_state in plant_final_state_set
#print('---------------------------------------------')
#print(_A.plant_abs.get_ival_constraints(abs_state.ps))
#print('---------------------------------------------')
pabs_ic = _A.plant_abs.get_ival_cons_abs_state(abs_state.plant_state)
intersection = pabs_ic & final_cons
return not(intersection is None or intersection.zero_measure)
# ##!!##logger.debug('{0}initial{0}\n{1}'.format('=' * 10, plant_initial_state_set))
return (set(initial_state_list), is_final)
def overapprox_x0(num_dims, prop, pwa_trace, solver=gopts.opt_engine):
A_ub, b_ub = pwatrace2lincons(pwa_trace, num_dims, prop)
num_opt_vars = A_ub.shape[1]
nvars = num_dims.x + num_dims.pi
#directions = [(1, 0), (-1, 0), (0, 1), (0, -1)]
I = np.eye(nvars)
directions = np.vstack((I, -I))
left_over_vars = num_opt_vars - nvars
directions_ext = np.pad(directions, [(0, 0), (0, left_over_vars)],
'constant')
# LP structure: A_ub [x x'] <= b_ub
x_arr = np.array(sym.symbols(['x{}'.format(i) for i in range(nvars)]))
A_ub, b_ub = truncate(A_ub, b_ub)
res = solve_mult_lp(lpfun(solver), directions_ext, A_ub, b_ub)
l = len(res)
assert (l % 2 == 0)
#min_directions, max_directions = np.split(directions, l/2, axis=1)
min_res, max_res = res[:l // 2], res[l // 2:]
ranges = []
for di, rmin, rmax in zip(directions, min_res, max_res):
if (rmin.success and rmax.success):
print('{} \in [{}, {}]'.format(np.dot(di, x_arr), rmin.fun,
-rmax.fun))
ranges.append([rmin.fun, -rmax.fun])
else:
if settings.debug:
print('LP failed')
print('rmin status:', rmin.status)
print('rmax status:', rmax.status)
return None
#raise e
# For python2/3 compatibility
# try:
# input = raw_input
# except NameError:
# pass
# prompt = input('load the prompt? (y/Y)')
# if prompt.lower() == 'y':
# import IPython
# IPython.embed()
#ranges = [[0.00416187, 0.00416187],[3.47047152,3.47047152],[9.98626028,9.98626028],[4.98715449,4.98715449]]
r = np.asarray(ranges)
# due to numerical errors, the interval can be malformed
try:
ret_val = cons.IntervalCons(r[:, 0], r[:, 1])
except:
#from IPython import embed
err.warn('linprog fp failure: Malformed Interval! Please fix.')
#embed()
return None
return ret_val
def get_all_path_generator(
G,
source_list,
sink_list,
max_depth,
max_paths
):
# Define super source and sink nodes
# A Super source node has a directed edge to each source node in the
# source_list
# Similarily, a Super sink node has a directed edge from each sink node
# in the sink_list
dummy_super_source_node = 'source'
dummy_super_sink_node = 'sink'
num_source_nodes = len(source_list)
num_sink_nodes = len(sink_list)
# increment max_depth by 2 to accommodate edges from 'super source' and
# to 'super sink'
max_depth += 2
# Add edges:
# \forall source \in source_list. super source node -> source
edge_list = zip([dummy_super_source_node] * num_source_nodes,
source_list)
G.add_edges_from(edge_list)
if __debug__:
print >> sys.stderr, 'source -> list'
for e in edge_list:
print >> sys.stderr, e
# Add edges:
# \forall sink \in sink_list. sink -> super sink node
edge_list = zip(sink_list, [dummy_super_sink_node] * num_sink_nodes)
G.add_edges_from(edge_list)
if __debug__:
print >> sys.stderr, 'sink -> list'
for e in edge_list:
print >> sys.stderr, e
if __debug__:
#print the graph first
print >>sys.stderr, 'Printing graph...'
for e in G.G.edges():
s, t = e
print >>sys.stderr, '{}, {}'.format(G.v_attr[s], G.v_attr[t])
print >>sys.stderr, 'Printing graph...done'
path_it = G.all_shortest_paths(dummy_super_source_node,
dummy_super_sink_node,
)
if __debug__:
print >> sys.stderr, 'path list'
paths = list(path_it)
for path in paths[0:max_paths]:
p = [G.v_attr[v] for v in path]
print >> sys.stderr, p
path_it = (i for i in paths)
#############################################################
# TODO: Remove the extra step to count paths
# It is there just as a debug print
paths = list(U.bounded_iter(path_it, max_paths))
num_paths = len(paths)
err.warn('counting paths...found: {}'.format(num_paths))
def path_gen():
for path in paths:
p = [G.v_attr[v] for v in path]
yield p[1:-1]
# END: CODE TO BE REMOVED
# CORRECT CODE BELOW
# def path_gen():
# for path in U.bounded_iter(path_it, max_paths):
# p = [G.v_attr[v] for v in path]
# yield p[1:-1]
#############################################################
return path_gen()
def exec_ops(self, algo, pages, gen_graphs, no_clean, debug):
logt = []
logn = []
opst = []
opsn = []
graphs = []
api = API(logn, logt, gen_graphs, graphs, debug)
if algo == 'simple':
tree = SimpleBST(api)
elif algo == 'rb':
tree = RedBlackBST(api)
elif algo == 'splay':
tree = SplayBST(api)
elif algo == 'avl':
err.err("Algorithm not yet implemented")
elif algo == 'wavl':
err.err("Algorithm not yet implemented")
elif algo == 'tango':
err.err("Algorithm not yet implemented")
elif algo == 'static':
err.err("Algorithm not yet implemented")
time = 1
for op in self.ops:
if debug > 2:
tree_str = str(tree)
if debug > 1:
err.log("operation #" + str(time) + ": " + str(op))
api.reset()
if op.op == 'ins':
opst.append(time)
opsn.append(op.arg)
api.set_time(time)
api.set_log_on()
tree.insert(op.arg)
api.set_log_off()
time += 1
elif op.op == 'sea':
opst.append(time)
opsn.append(op.arg)
api.set_time(time)
api.set_log_on()
tree.search(op.arg)
api.set_log_off()
time += 1
elif op.op == 'del':
opst.append(time)
opsn.append(op.arg)
api.set_time(time)
api.set_log_on()
tree.delete(op.arg)
api.set_log_off()
time += 1
if debug == 2:
err.warn(tree)
if debug > 2:
err.log("Before op, tree was:")
err.warn(tree_str)
err.log("After op, tree is:")
err.warn(tree)
err.warn("Beginning tree verification:")
if tree.verify_tree():
err.warn("Verified")
else:
err.err("Issue found when verifying tree!")
if gen_graphs and pages:
api.viz()
if gen_graphs and not pages:
api.viz()
if debug == 1 and len(self.ops) < 50:
err.warn(tree)
plot.plot(logn, logt, opsn, opst, pages, graphs, no_clean, debug)