def test_Quartic():
w = testmod.Quartic()
assert (w(0, 0) == approx(1))
assert (w(50, 0) == approx((15 / 16)**2))
assert (w(0, 50) == approx((15 / 16)**2))
assert (w(0, 200) == 0)
assert (w(100, 100) == approx(0.25))
assert (w(150, 150) == 0)
def predict(self, time):
grid_pred = retro.RetroHotSpotGrid(grid=self.masked_grid)
grid_pred.data = self.points
grid_pred.weight = retro.Quartic(bandwidth=1000)
grid_risk = grid_pred.predict(start_time=time -
self.time_window_length,
end_time=time)
grid_risk.mask_with(self.masked_grid)
return grid_risk
def runRhsModel(training_data, grid, bandwidth=250, rand_seed=None):
# Set RNG seed if given
if rand_seed != None:
np.random.seed(rand_seed)
grid_cells = getRegionCells(grid)
# Obtain model and prediction on grid cells
rhs_pred = retro.RetroHotSpot()
rhs_pred.data = training_data
rhs_pred.weight = retro.Quartic(bandwidth=bandwidth)
rhs_risk = rhs_pred.predict()
rhs_grid_risk = open_cp.predictors.grid_prediction(rhs_risk, grid)
rhs_grid_risk_matrix = rhs_grid_risk.intensity_matrix
# Sort cellcoords by risk in intensity matrix, highest risk first
return sortCellsByRiskMatrix(grid_cells, rhs_grid_risk_matrix)
def averageRhsModels(training_data, grid, bandwidth=250, num_runs=1, \
rand_seed_list = None):
#
if rand_seed_list == None:
rand_seed_list = range(num_runs)
else:
num_runs = len(rand_seed_list)
grid_cells = getRegionCells(grid)
# Obtain model and prediction on grid cells
rhs_pred = retro.RetroHotSpot()
rhs_pred.data = training_data
rhs_pred.weight = retro.Quartic(bandwidth=bandwidth)
rhs_risk = rhs_pred.predict()
matrix_collection = []
for rand_seed in rand_seed_list:
np.random.seed(rand_seed)
rhs_grid_risk = open_cp.predictors.grid_prediction(rhs_risk, grid)
rhs_grid_risk_matrix = rhs_grid_risk.intensity_matrix
matrix_collection.append(rhs_grid_risk_matrix)
master_matrix = sum(matrix_collection)
for m in matrix_collection:
print(m[12:20, 12:20])
print(master_matrix[12:20, 12:20])
for m in matrix_collection:
print(sortCellsByRiskMatrix(grid_cells, m)[:8])
print(sortCellsByRiskMatrix(grid_cells, master_matrix)[:8])
sys.exit(0)
# Sort cellcoords by risk in intensity matrix, highest risk first
return sortCellsByRiskMatrix(grid_cells, master_matrix)
def test_Quartic_can_pass_array():
w = testmod.Quartic()
xcoords = np.array([0, 50, 0, 0, 100, 150])
ycoords = np.array([0, 0, 50, 200, 100, 150])
expected = np.array([1, (15 / 16)**2, (15 / 16)**2, 0, 0.25, 0])
np.testing.assert_allclose(w(xcoords, ycoords), expected)