def test_as_dict(toy_config):
amp = toy_config.get_amplitude()
dg = toy_config.get_decay()
data = toy_config.get_data("data")[0]
idx, cached_data = build_amp.build_angle_amp_matrix(dg, data)
dic = build_amp.amp_matrix_as_dict(idx, cached_data)
assert len(dic) == len(cached_data)
def main():
config = ConfigLoader("config.yml")
decay = config.get_amplitude().decay_group
data = config.get_data("phsp")
ret = []
for i in data:
cached_amp = build_angle_amp_matrix(decay, i)
ret.append(data_to_numpy(cached_amp))
idx = config.get_data_index("angle", "R_BC/B")
ang = data_index(data[0], idx)
np.savez("phsp.npz", ret)
for k, v in ret[0].items():
for i, amp in enumerate(v):
w = np.abs(amp)**2
w = np.sum(np.reshape(w, (amp.shape[0], -1)), axis=-1)
plt.hist(
np.cos(ang["beta"]),
weights=w,
bins=20,
histtype="step",
label="{}: {}".format(k, i),
)
plt.savefig("angle_costheta.png")
def test_build_amp2s(toy_config):
# assert False
amp = toy_config.get_amplitude()
dg = toy_config.get_decay()
data = toy_config.get_data("data")[0]
amp2s = build_amp.build_amp2s(dg)
_, cached_data = build_amp.build_angle_amp_matrix(dg, data)
amp2s.python_function(data, cached_data)
def sum_nll_grad_bacth(self, data):
data_id = id(data)
data = list(data)
weight = [i.get("weight", tf.ones((data_shape(i), ))) for i in data]
if data_id not in self.cached_data:
self.cached_data[data_id] = [
build_amp.build_angle_amp_matrix(self.Amp.decay_group, i)[1]
for i in data
]
ln_data, g_ln_data = sum_gradient_data2(
self.cached_amp,
self.Amp.trainable_variables,
data,
self.cached_data[data_id],
weight=weight,
trans=clip_log,
)
return -ln_data, [-i for i in g_ln_data]
def sum_log_integral_grad_batch(self, mcdata, ndata):
mc_id = id(mcdata)
mcdata = list(mcdata)
mc_weight = [i["weight"] for i in mcdata]
if mc_id not in self.cached_data:
self.cached_data[mc_id] = [
build_amp.build_angle_amp_matrix(self.Amp.decay_group, i)[1]
for i in mcdata
]
int_mc, g_int_mc = sum_gradient_data2(
self.cached_amp,
self.Amp.trainable_variables,
mcdata,
self.cached_data[mc_id],
weight=mc_weight,
)
return tf.math.log(int_mc) * ndata, [
ndata / int_mc * i for i in g_int_mc
]
def test_build_angle_amplitude(toy_config):
data = toy_config.get_data("data")
dec = toy_config.get_amplitude().decay_group
amp_dict = build_amp.build_angle_amp_matrix(dec, data[0])
assert len(amp_dict) == 3
def nll_grad_batch(self, data, mcdata, weight, mc_weight):
"""
``self.nll_grad()`` is replaced by this one???
.. math::
- \\frac{\\partial \\ln L}{\\partial \\theta_k } =
-\\sum_{x_i \\in data } w_i \\frac{\\partial}{\\partial \\theta_k} \\ln f(x_i;\\theta_k)
+ (\\sum w_j ) \\left( \\frac{ \\partial }{\\partial \\theta_k} \\sum_{x_i \\in mc} f(x_i;\\theta_k) \\right)
\\frac{1}{ \\sum_{x_i \\in mc} f(x_i;\\theta_k) }
:param data:
:param mcdata:
:param weight:
:param mc_weight:
:return:
"""
sw = tf.reduce_sum([tf.reduce_sum(i) for i in weight])
data_id = id(data)
data = list(data)
weight = list(weight)
if data_id not in self.cached_data:
self.cached_data[data_id] = [
build_amp.build_angle_amp_matrix(self.Amp.decay_group, i)[1]
for i in data
]
ln_data, g_ln_data = sum_gradient_data2(
self.cached_amp,
self.Amp.trainable_variables,
data,
self.cached_data[data_id],
weight=weight,
trans=clip_log,
)
# print(ln_data, ln_data2, np.allclose(g_ln_data, g_ln_data2))
mc_id = id(mcdata)
mcdata = list(mcdata)
if mc_id not in self.cached_data:
self.cached_data[mc_id] = [
build_amp.build_angle_amp_matrix(self.Amp.decay_group, i)[1]
for i in mcdata
]
int_mc, g_int_mc = sum_gradient_data2(
self.cached_amp,
self.Amp.trainable_variables,
mcdata,
self.cached_data[mc_id],
weight=mc_weight,
)
# int_mc2, g_int_mc2 = sum_gradient(self.Amp, mcdata,
# self.Amp.trainable_variables, weight=mc_weight)
#
# print("exp", int_mc, g_int_mc)
# print("now", int_mc2, g_int_mc2)
sw = tf.cast(sw, ln_data.dtype)
g = list(
map(lambda x: -x[0] + sw * x[1] / int_mc, zip(g_ln_data,
g_int_mc)))
nll = -ln_data + sw * tf.math.log(int_mc)
return nll, g