def write_robberies(writer, row):
print row
writer.writerow(["year", "income0", "income1", "coeff0", "coeff1", "coeff2"])
incomes = np.linspace(0, 1e6, 20)
for year in range(2010, 2100+1):
crime_fraction = np.sin(np.pi*incomes / 1e6) + random.uniform(.5, 1.5) * (1 + (year - 2010) / 200.0)
crime = 200 * crime_fraction
spline = UnivariateSpline(incomes, crime, s=10, k=2)
conditional = SplineModelConditional.make_conditional_from_spline(spline, [0, 1e6])
for jj in range(conditional.size()):
row = [year, conditional.y0s[jj], conditional.y1s[jj]]
row.extend(conditional.coeffs[jj])
writer.writerow(row)
def skew_gaussian_construct(ys, lps, low_segment, high_segment):
mid_segment = SplineModelConditional.make_conditional_from_spline(InterpolatedUnivariateSpline(ys, lps, k=2), (low_segment.y1s[0], high_segment.y0s[0]))
conditional = SplineModelConditional()
conditional.add_segment(low_segment.y0s[0], low_segment.y1s[0], copy.copy(low_segment.coeffs[0]))
for ii in range(mid_segment.size()):
conditional.add_segment(mid_segment.y0s[ii], mid_segment.y1s[ii], mid_segment.coeffs[ii])
conditional.add_segment(high_segment.y0s[0], high_segment.y1s[0], copy.copy(high_segment.coeffs[0]))
try:
conditional.rescale()
except:
return None
return conditional
def make_conditional(header, row, limits):
# Look for a special case
conditional = FeaturesInterpreter.features_to_gaussian(header, row, limits)
if conditional is not None:
return conditional
conditional = FeaturesInterpreter.features_to_exponential(header, row, limits)
if conditional is not None:
return conditional
conditional = FeaturesInterpreter.features_to_uniform(header, row, limits)
if conditional is not None:
return conditional
spline = FeaturesInterpreter.best_spline(header, row, limits)
conditional = SplineModelConditional.make_conditional_from_spline(spline, limits)
return conditional.rescale()
def skew_gaussian_construct(ys, lps, low_segment, high_segment):
mid_segment = SplineModelConditional.make_conditional_from_spline(
InterpolatedUnivariateSpline(ys, lps, k=2),
(low_segment.y1s[0], high_segment.y0s[0]))
conditional = SplineModelConditional()
conditional.add_segment(low_segment.y0s[0], low_segment.y1s[0],
copy.copy(low_segment.coeffs[0]))
for ii in range(mid_segment.size()):
conditional.add_segment(mid_segment.y0s[ii], mid_segment.y1s[ii],
mid_segment.coeffs[ii])
conditional.add_segment(high_segment.y0s[0], high_segment.y1s[0],
copy.copy(high_segment.coeffs[0]))
try:
conditional.rescale()
except:
return None
return conditional
def make_conditional(header, row, limits):
# Look for a special case
conditional = FeaturesInterpreter.features_to_gaussian(
header, row, limits)
if conditional is not None:
return conditional
conditional = FeaturesInterpreter.features_to_exponential(
header, row, limits)
if conditional is not None:
return conditional
conditional = FeaturesInterpreter.features_to_uniform(
header, row, limits)
if conditional is not None:
return conditional
spline = FeaturesInterpreter.best_spline(header, row, limits)
conditional = SplineModelConditional.make_conditional_from_spline(
spline, limits)
return conditional.rescale()
