def main():
np.random.seed(123475)
# sample a random rate matrix
state_count = 3
edge_count = 3
node_count = edge_count + 1
#Q = sample_rate_matrix(state_count)
Q = sample_reversible_rate_matrix(state_count)
p = equilibrium(Q)
expected_rate = -p.dot(np.diag(Q))
print('expected rate:', expected_rate)
Q = Q / expected_rate
np.fill_diagonal(Q, 0)
# use ad hoc data
probability_array = [[[1, 1, 1], [1, 0, 0], [1, 0, 0], [1, 0, 0]],
[[1, 1, 1], [0, 1, 0], [1, 0, 0], [1, 0, 0]],
[[1, 1, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1]]]
site_weights = [.7, .2, .1]
edges = [[0, 1], [0, 2], [0, 3]]
coefficients = [.01, .01, .01]
d = {
"model_and_data": {
"edges": edges,
"edge_rate_coefficients": coefficients,
"rate_matrix": Q.tolist(),
"probability_array": probability_array
},
"site_reduction": {
"aggregation": site_weights
}
}
print(d)
for i in range(100):
s = arbplf_em_update(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
y = df.value.values.tolist()
d['model_and_data']['edge_rate_coefficients'] = y
print('coefficients updated by EM:', y)
s = arbplf_newton_refine(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
y = df.value.values.tolist()
print('coefficients updated by newton refinement:', y)
d['trans_reduction'] = {
'selection': [[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]],
'aggregation': 'sum'
}
d['model_and_data']['edge_rate_coefficients'] = y
s = arbplf_trans(json.dumps(d))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
y = df.value.values.tolist()
print('conditionally expected transition counts:', y)
def main():
np.random.seed(123475)
# sample a random rate matrix
state_count = 3
edge_count = 3
node_count = edge_count + 1
# Q = sample_rate_matrix(state_count)
Q = sample_reversible_rate_matrix(state_count)
p = equilibrium(Q)
expected_rate = -p.dot(np.diag(Q))
print("expected rate:", expected_rate)
Q = Q / expected_rate
np.fill_diagonal(Q, 0)
# use ad hoc data
probability_array = [
[[1, 1, 1], [1, 0, 0], [1, 0, 0], [1, 0, 0]],
[[1, 1, 1], [0, 1, 0], [1, 0, 0], [1, 0, 0]],
[[1, 1, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1]],
]
site_weights = [0.7, 0.2, 0.1]
edges = [[0, 1], [0, 2], [0, 3]]
coefficients = [0.01, 0.01, 0.01]
d = {
"model_and_data": {
"edges": edges,
"edge_rate_coefficients": coefficients,
"rate_matrix": Q.tolist(),
"probability_array": probability_array,
},
"site_reduction": {"aggregation": site_weights},
}
print(d)
for i in range(100):
s = arbplf_em_update(json.dumps(d))
df = pd.read_json(StringIO(s), orient="split", precise_float=True)
y = df.value.values.tolist()
d["model_and_data"]["edge_rate_coefficients"] = y
print("coefficients updated by EM:", y)
s = arbplf_newton_refine(json.dumps(d))
df = pd.read_json(StringIO(s), orient="split", precise_float=True)
y = df.value.values.tolist()
print("coefficients updated by newton refinement:", y)
d["trans_reduction"] = {"selection": [[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]], "aggregation": "sum"}
d["model_and_data"]["edge_rate_coefficients"] = y
s = arbplf_trans(json.dumps(d))
df = pd.read_json(StringIO(s), orient="split", precise_float=True)
y = df.value.values.tolist()
print("conditionally expected transition counts:", y)
def main():
xs = np.linspace(1e-5, 1, 100)
ts = -2 * np.log(xs)
arr = []
for i, t in enumerate(ts):
s = arbplf_ll(get_json_input(t))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
arr.append(df.value.values[0])
lines = plt.plot(xs, arr, 'blue')
plt.ylabel("log likelihood")
plt.xlabel("x = exp(-0.5 t)")
plt.savefig('out00.svg', transparent=True)
# local optima
for i, t in enumerate((0.1, 6.0)):
s = arbplf_newton_refine(get_json_input(t))
df = pd.read_json(StringIO(s), orient='split', precise_float=True)
u = df.value.values[0]
print('local optimum', i, ':')
print(' initial guess:', t)
print(' refined isolated interior local optimum:')
print(' t = {:.16}'.format(u))
print(' x =', np.exp(-0.5 * u))
