def main():
k = 4
print 'reading state distribution from stdin...'
rows = []
for line in sys.stdin.readlines():
AB, Ab, aB, ab, p = line.split()
row = [int(AB), int(Ab), int(aB), int(ab), float(p)]
rows.append(row)
N = sum(rows[0][:-1])
print 'defining the state vectors...'
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
m_AB_ab = np.zeros(N+1)
m_AB_Ab = np.zeros(N+1)
for AB, Ab, aB, ab, p in rows:
m_AB_ab[AB+ab] += p
m_AB_Ab[AB+Ab] += p
print 'marginal distribution of AB+ab:'
print m_AB_ab
print
print 'marginal distribution of AB+Ab:'
print m_AB_Ab
print
plot_AB_plus_Ab(rows)
plot_AB_given_only_AB_and_Ab(rows)
plot_AB_given_half_AB_plus_Ab(rows)
plot_reciprocal_variance_AB_given_AB_plus_Ab(rows)
def main(args):
alpha = args.alpha
N = args.N
k = 3
print 'alpha:', alpha
print 'N:', N
print 'k:', k
print
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
R_mut = wrightcore.create_mutation_abc(M, T)
R_drift = wrightcore.create_moran_drift_rate_k3(M, T)
Q = alpha * R_mut + R_drift
# pick out the correct eigenvector
W, V = scipy.linalg.eig(Q.T)
w, v = min(zip(np.abs(W), V.T))
print 'rate matrix:'
print Q
print
print 'transpose of rate matrix:'
print Q.T
print
print 'eigendecomposition of transpose of rate matrix as integers:'
print scipy.linalg.eig(Q.T)
print
print 'transpose of rate matrix in mathematica notation:'
print MatrixUtil.m_to_mathematica_string(Q.T.astype(int))
print
print 'abs eigenvector corresponding to smallest abs eigenvalue:'
print np.abs(v)
print
def main():
k = 4
print 'reading state distribution from stdin...'
rows = []
for line in sys.stdin.readlines():
AB, Ab, aB, ab, p = line.split()
row = [int(AB), int(Ab), int(aB), int(ab), float(p)]
rows.append(row)
N = sum(rows[0][:-1])
print 'defining the state vectors...'
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
m_AB_ab = np.zeros(N + 1)
m_AB_Ab = np.zeros(N + 1)
for AB, Ab, aB, ab, p in rows:
m_AB_ab[AB + ab] += p
m_AB_Ab[AB + Ab] += p
print 'marginal distribution of AB+ab:'
print m_AB_ab
print
print 'marginal distribution of AB+Ab:'
print m_AB_Ab
print
plot_AB_plus_Ab(rows)
plot_AB_given_only_AB_and_Ab(rows)
plot_AB_given_half_AB_plus_Ab(rows)
plot_reciprocal_variance_AB_given_AB_plus_Ab(rows)
def main(args):
alpha = args.alpha
N = args.N
k = 3
print 'alpha:', alpha
print 'N:', N
print 'k:', k
print
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
R_mut = wrightcore.create_mutation_abc(M, T)
R_drift = wrightcore.create_moran_drift_rate_k3(M, T)
Q = alpha * R_mut + R_drift
# pick out the correct eigenvector
W, V = scipy.linalg.eig(Q.T)
w, v = min(zip(np.abs(W), V.T))
print 'rate matrix:'
print Q
print
print 'transpose of rate matrix:'
print Q.T
print
print 'eigendecomposition of transpose of rate matrix as integers:'
print scipy.linalg.eig(Q.T)
print
print 'transpose of rate matrix in mathematica notation:'
print MatrixUtil.m_to_mathematica_string(Q.T.astype(int))
print
print 'abs eigenvector corresponding to smallest abs eigenvalue:'
print np.abs(v)
print
def do_full_simplex_then_collapse(mutrate, popsize):
#mutrate = 0.01
#mutrate = 0.2
#mutrate = 10
#mutrate = 100
#mutrate = 1
N = popsize
k = 4
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
# Create the joint site pair mutation rate matrix.
R = mutrate * wrightcore.create_mutation(M, T)
# Create the joint site pair drift transition matrix.
lmcs = wrightcore.get_lmcs(M)
lps = wrightcore.create_selection_neutral(M)
log_drift = wrightcore.create_neutral_drift(lmcs, lps, M)
# Define the drift and mutation transition matrices.
P_drift = np.exp(log_drift)
P_mut = scipy.linalg.expm(R)
# Define the composite per-generation transition matrix.
P = np.dot(P_mut, P_drift)
# Solve a system of equations to find the stationary distribution.
v = MatrixUtil.get_stationary_distribution(P)
for state, value in zip(M, v):
print state, value
# collapse the two middle states
nstates_collapsed = multinomstate.get_nstates(N, k - 1)
M_collapsed = np.array(list(multinomstate.gen_states(N, k - 1)), dtype=int)
T_collapsed = multinomstate.get_inverse_map(M_collapsed)
v_collapsed = np.zeros(nstates_collapsed)
for i, bigstate in enumerate(M):
AB, Ab, aB, ab = bigstate.tolist()
Ab_aB = Ab + aB
j = T_collapsed[AB, Ab_aB, ab]
v_collapsed[j] += v[i]
for state, value in zip(M_collapsed, v_collapsed):
print state, value
# draw an equilateral triangle
#drawtri(M_collapsed, T_collapsed, v_collapsed)
#test_mesh()
return M_collapsed, T_collapsed, v_collapsed
def do_full_simplex_then_collapse(mutrate, popsize):
#mutrate = 0.01
#mutrate = 0.2
#mutrate = 10
#mutrate = 100
#mutrate = 1
N = popsize
k = 4
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
# Create the joint site pair mutation rate matrix.
R = mutrate * wrightcore.create_mutation(M, T)
# Create the joint site pair drift transition matrix.
lmcs = wrightcore.get_lmcs(M)
lps = wrightcore.create_selection_neutral(M)
log_drift = wrightcore.create_neutral_drift(lmcs, lps, M)
# Define the drift and mutation transition matrices.
P_drift = np.exp(log_drift)
P_mut = scipy.linalg.expm(R)
# Define the composite per-generation transition matrix.
P = np.dot(P_mut, P_drift)
# Solve a system of equations to find the stationary distribution.
v = MatrixUtil.get_stationary_distribution(P)
for state, value in zip(M, v):
print state, value
# collapse the two middle states
nstates_collapsed = multinomstate.get_nstates(N, k-1)
M_collapsed = np.array(list(multinomstate.gen_states(N, k-1)), dtype=int)
T_collapsed = multinomstate.get_inverse_map(M_collapsed)
v_collapsed = np.zeros(nstates_collapsed)
for i, bigstate in enumerate(M):
AB, Ab, aB, ab = bigstate.tolist()
Ab_aB = Ab + aB
j = T_collapsed[AB, Ab_aB, ab]
v_collapsed[j] += v[i]
for state, value in zip(M_collapsed, v_collapsed):
print state, value
# draw an equilateral triangle
#drawtri(M_collapsed, T_collapsed, v_collapsed)
#test_mesh()
return M_collapsed, T_collapsed, v_collapsed
def main():
# use standard notation
Nmu = 1.0
N = 120
mu = Nmu / float(N)
print 'N*mu:', Nmu
print 'N:', N
print
# multiply the rate matrix by this scaling factor
m_factor = mu
# use the moran drift
distn_helper = moran_distn_helper
# get properties of the collapsed diamond process
k = 3
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
R_mut = m_factor * wrightcore.create_mutation_collapsed(M, T)
v = distn_helper(M, T, R_mut)
for Ab_aB in range(N + 1):
nremaining = N - Ab_aB
# compute the volume for normalization
volume = 0.0
for AB in range(nremaining + 1):
ab = nremaining - AB
volume += v[T[AB, Ab_aB, ab]]
# print some info
print 'X_1 + X_4 =', Ab_aB, '/', N
print 'probability =', volume
print 'Y = X_2 / (1 - (X_1 + X_4)) = X_2 / (X_2 + X_3)'
if not nremaining:
print 'conditional distribution of Y is undefined'
else:
# compute the conditional moments
m1 = 0.0
m2 = 0.0
for AB in range(nremaining + 1):
ab = nremaining - AB
p = v[T[AB, Ab_aB, ab]] / volume
x = AB / float(nremaining)
m1 += x * p
m2 += x * x * p
# print some info
print 'conditional E(Y) =', m1
print 'conditional E(Y^2) =', m2
print 'conditional V(Y) =', m2 - m1 * m1
print
def main():
# use standard notation
Nmu = 1.0
N = 120
mu = Nmu / float(N)
print 'N*mu:', Nmu
print 'N:', N
print
# multiply the rate matrix by this scaling factor
m_factor = mu
# use the moran drift
distn_helper = moran_distn_helper
# get properties of the collapsed diamond process
k = 3
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
R_mut = m_factor * wrightcore.create_mutation_collapsed(M, T)
v = distn_helper(M, T, R_mut)
for Ab_aB in range(N+1):
nremaining = N - Ab_aB
# compute the volume for normalization
volume = 0.0
for AB in range(nremaining+1):
ab = nremaining - AB
volume += v[T[AB, Ab_aB, ab]]
# print some info
print 'X_1 + X_4 =', Ab_aB, '/', N
print 'probability =', volume
print 'Y = X_2 / (1 - (X_1 + X_4)) = X_2 / (X_2 + X_3)'
if not nremaining:
print 'conditional distribution of Y is undefined'
else:
# compute the conditional moments
m1 = 0.0
m2 = 0.0
for AB in range(nremaining+1):
ab = nremaining - AB
p = v[T[AB, Ab_aB, ab]] / volume
x = AB / float(nremaining)
m1 += x*p
m2 += x*x*p
# print some info
print 'conditional E(Y) =', m1
print 'conditional E(Y^2) =', m2
print 'conditional V(Y) =', m2 - m1*m1
print
def get_full_simplex(m_factor, N, distn_helper):
"""
Note that this uses the non-moran formulation of drift.
The distn_helper function taken as an argument is expected
to be either moran_distn_helper or wright_distn_helper.
@param m_factor: the mutation rate matrix is multiplied by this number
@param N: population size
@param distn_helper: a function (M, T, R_mut) -> v
@return: M, T, v
"""
k = 4
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
R_mut = m_factor * wrightcore.create_mutation(M, T)
v = distn_helper(M, T, R_mut)
return M, T, v
def get_full_simplex(m_factor, N, distn_helper):
"""
Note that this uses the non-moran formulation of drift.
The distn_helper function taken as an argument is expected
to be either moran_distn_helper or wright_distn_helper.
@param m_factor: the mutation rate matrix is multiplied by this number
@param N: population size
@param distn_helper: a function (M, T, R_mut) -> v
@return: M, T, v
"""
k = 4
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
R_mut = m_factor * wrightcore.create_mutation(M, T)
v = distn_helper(M, T, R_mut)
return M, T, v
def collapse_diamond(N, M, v):
"""
Collapse the middle two states.
@param N: population size
@param M: index to state vector
@param v: a distribution over a 3-simplex
@return: a distribution over a 2-simplex
"""
k = 4
nstates_collapsed = multinomstate.get_nstates(N, k - 1)
M_collapsed = np.array(list(multinomstate.gen_states(N, k - 1)), dtype=int)
T_collapsed = multinomstate.get_inverse_map(M_collapsed)
v_collapsed = np.zeros(nstates_collapsed)
for i, bigstate in enumerate(M):
AB, Ab, aB, ab = bigstate.tolist()
Ab_aB = Ab + aB
j = T_collapsed[AB, Ab_aB, ab]
v_collapsed[j] += v[i]
return v_collapsed
def collapse_diamond(N, M, v):
"""
Collapse the middle two states.
@param N: population size
@param M: index to state vector
@param v: a distribution over a 3-simplex
@return: a distribution over a 2-simplex
"""
k = 4
nstates_collapsed = multinomstate.get_nstates(N, k-1)
M_collapsed = np.array(list(multinomstate.gen_states(N, k-1)), dtype=int)
T_collapsed = multinomstate.get_inverse_map(M_collapsed)
v_collapsed = np.zeros(nstates_collapsed)
for i, bigstate in enumerate(M):
AB, Ab, aB, ab = bigstate.tolist()
Ab_aB = Ab + aB
j = T_collapsed[AB, Ab_aB, ab]
v_collapsed[j] += v[i]
return v_collapsed
def collapse_side(N, M, v):
"""
Collapse two pairs of states.
@param N: population size
@param M: index to state vector
@param v: a distribution over a 3-simplex
@return: a distribution over a 1-simplex
"""
k = 4
nstates_collapsed = multinomstate.get_nstates(N, k-2)
M_collapsed = np.array(list(multinomstate.gen_states(N, k-2)), dtype=int)
T_collapsed = multinomstate.get_inverse_map(M_collapsed)
v_collapsed = np.zeros(nstates_collapsed)
for i, bigstate in enumerate(M):
AB, Ab, aB, ab = bigstate.tolist()
AB_Ab = AB + Ab
aB_ab = aB + ab
j = T_collapsed[AB_Ab, aB_ab]
v_collapsed[j] += v[i]
return v_collapsed
def collapse_side(N, M, v):
"""
Collapse two pairs of states.
@param N: population size
@param M: index to state vector
@param v: a distribution over a 3-simplex
@return: a distribution over a 1-simplex
"""
k = 4
nstates_collapsed = multinomstate.get_nstates(N, k - 2)
M_collapsed = np.array(list(multinomstate.gen_states(N, k - 2)), dtype=int)
T_collapsed = multinomstate.get_inverse_map(M_collapsed)
v_collapsed = np.zeros(nstates_collapsed)
for i, bigstate in enumerate(M):
AB, Ab, aB, ab = bigstate.tolist()
AB_Ab = AB + Ab
aB_ab = aB + ab
j = T_collapsed[AB_Ab, aB_ab]
v_collapsed[j] += v[i]
return v_collapsed
def do_collapsed_simplex(scaled_mut, N):
"""
@param N: population size
"""
k = 3
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
# Create the joint site pair mutation rate matrix.
# This is scaled so that there are about popsize mutations per generation.
R_mut_raw = wrightcore.create_mutation_collapsed(M, T)
R_mut = (scaled_mut / float(N)) * R_mut_raw
# Create the joint site pair drift transition matrix.
lmcs = wrightcore.get_lmcs(M)
lps = wrightcore.create_selection_neutral(M)
#log_drift = wrightcore.create_neutral_drift(lmcs, lps, M)
# Define the drift and mutation transition matrices.
#P_drift = np.exp(log_drift)
#P_mut = scipy.linalg.expm(R)
# Define the composite per-generation transition matrix.
#P = np.dot(P_mut, P_drift)
# Solve a system of equations to find the stationary distribution.
#v = MatrixUtil.get_stationary_distribution(P)
# Try a new thing.
# The raw drift matrix is scaled so that there are about N*N
# replacements per generation.
generation_rate = 1.0
R_drift_raw = wrightcore.create_moran_drift_rate_k3(M, T)
R_drift = (generation_rate / float(N)) * R_drift_raw
#FIXME: you should get the stationary distn directly from the rate matrix
P = scipy.linalg.expm(R_mut + R_drift)
v = MatrixUtil.get_stationary_distribution(P)
"""
for state, value in zip(M, v):
print state, value
"""
# draw an equilateral triangle
#drawtri(M, T, v)
return M, T, v
def do_collapsed_simplex(scaled_mut, N):
"""
@param N: population size
"""
k = 3
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
# Create the joint site pair mutation rate matrix.
# This is scaled so that there are about popsize mutations per generation.
R_mut_raw = wrightcore.create_mutation_collapsed(M, T)
R_mut = (scaled_mut / float(N)) * R_mut_raw
# Create the joint site pair drift transition matrix.
lmcs = wrightcore.get_lmcs(M)
lps = wrightcore.create_selection_neutral(M)
#log_drift = wrightcore.create_neutral_drift(lmcs, lps, M)
# Define the drift and mutation transition matrices.
#P_drift = np.exp(log_drift)
#P_mut = scipy.linalg.expm(R)
# Define the composite per-generation transition matrix.
#P = np.dot(P_mut, P_drift)
# Solve a system of equations to find the stationary distribution.
#v = MatrixUtil.get_stationary_distribution(P)
# Try a new thing.
# The raw drift matrix is scaled so that there are about N*N
# replacements per generation.
generation_rate = 1.0
R_drift_raw = wrightcore.create_moran_drift_rate_k3(M, T)
R_drift = (generation_rate / float(N)) * R_drift_raw
#FIXME: you should get the stationary distn directly from the rate matrix
P = scipy.linalg.expm(R_mut + R_drift)
v = MatrixUtil.get_stationary_distribution(P)
"""
for state, value in zip(M, v):
print state, value
"""
# draw an equilateral triangle
#drawtri(M, T, v)
return M, T, v
def get_collapsed_diag_process_distn(m_factor, N, distn_helper):
k = 2
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
R_mut = m_factor * wrightcore.create_mutation_collapsed_diag(M, T)
return distn_helper(M, T, R_mut)
def main(args):
alpha = args.alpha
N = args.N
k = 4
print 'alpha:', alpha
print 'N:', N
print 'k:', k
print
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
R_mut = wrightcore.create_mutation(M, T)
R_drift = wrightcore.create_moran_drift_rate_k4(M, T)
Q = alpha * R_mut + R_drift
P = scipy.linalg.expm(Q)
v = MatrixUtil.get_stationary_distribution(P)
#
# Define the volumetric data using the stationary distribution.
max_prob = np.max(v)
d2 = np.zeros((N + 1, N + 1, N + 1, 4), dtype=float)
U = np.array([
[0, 0, 0],
[0, 1, 1],
[1, 0, 1],
[1, 1, 0],
], dtype=int)
for p, state in zip(v, M):
x, y, z = np.dot(state, U).tolist()
# r, g, b, alpha
d2[x, y, z] = np.array(
[
255 * (p / max_prob),
0,
0,
255 * (p / max_prob),
#100,
],
dtype=float)
#d2[x, y, z, 0] = 255 * (p / max_prob)
#d2[x, y, z, 1] = 0
#d2[x, y, z, 2] = 0
#d2[x, y, z, 3] = 100
# fill the empty states
for x in range(N + 1):
for y in range(N + 1):
for z in range(N + 1):
if (x + y + z) % 2 == 1:
p_accum = np.zeros(4, dtype=float)
n_accum = 0
for dx in (-1, 1):
if 0 <= x + dx <= N:
p_accum += d2[x + dx, y, z]
n_accum += 1
for dy in (-1, 1):
if 0 <= y + dy <= N:
p_accum += d2[x, y + dy, z]
n_accum += 1
for dz in (-1, 1):
if 0 <= z + dz <= N:
p_accum += d2[x, y, z + dz]
n_accum += 1
d2[x, y, z] = p_accum / n_accum
#
# Do things that the example application does.
app = QtGui.QApplication([])
w = gl.GLViewWidget()
w.opts['distance'] = 2 * N
w.show()
#
# a visual grid or something
#g = gl.GLGridItem()
#g.scale(10, 10, 1)
#w.addItem(g)
#
# Do some more things that the example application does.
vol = gl.GLVolumeItem(d2, sliceDensity=1, smooth=True)
#vol.translate(-5,-5,-10)
vol.translate(-0.5 * N, -0.5 * N, -0.5 * N)
w.addItem(vol)
#
# add an axis thingy
#ax = gl.GLAxisItem()
#w.addItem(ax)
if sys.flags.interactive != 1:
app.exec_()
def main(args):
alpha = args.alpha
N = args.N
k = 4
print 'alpha:', alpha
print 'N:', N
print 'k:', k
print
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
R_mut = wrightcore.create_mutation(M, T)
R_drift = wrightcore.create_moran_drift_rate_k4(M, T)
Q = alpha * R_mut + R_drift
P = scipy.linalg.expm(Q)
v = MatrixUtil.get_stationary_distribution(P)
#
# Define the volumetric data using the stationary distribution.
max_prob = np.max(v)
d2 = np.zeros((N+1, N+1, N+1, 4), dtype=float)
U = np.array([
[0, 0, 0],
[0, 1, 1],
[1, 0, 1],
[1, 1, 0],
], dtype=int)
for p, state in zip(v, M):
x, y, z = np.dot(state, U).tolist()
# r, g, b, alpha
d2[x, y, z] = np.array([
255 * (p / max_prob),
0,
0,
255 * (p / max_prob),
#100,
], dtype=float)
#d2[x, y, z, 0] = 255 * (p / max_prob)
#d2[x, y, z, 1] = 0
#d2[x, y, z, 2] = 0
#d2[x, y, z, 3] = 100
# fill the empty states
for x in range(N+1):
for y in range(N+1):
for z in range(N+1):
if (x + y + z) % 2 == 1:
p_accum = np.zeros(4, dtype=float)
n_accum = 0
for dx in (-1, 1):
if 0 <= x+dx <= N:
p_accum += d2[x+dx, y, z]
n_accum += 1
for dy in (-1, 1):
if 0 <= y+dy <= N:
p_accum += d2[x, y+dy, z]
n_accum += 1
for dz in (-1, 1):
if 0 <= z+dz <= N:
p_accum += d2[x, y, z+dz]
n_accum += 1
d2[x, y, z] = p_accum / n_accum
#
# Do things that the example application does.
app = QtGui.QApplication([])
w = gl.GLViewWidget()
w.opts['distance'] = 2*N
w.show()
#
# a visual grid or something
#g = gl.GLGridItem()
#g.scale(10, 10, 1)
#w.addItem(g)
#
# Do some more things that the example application does.
vol = gl.GLVolumeItem(d2, sliceDensity=1, smooth=True)
#vol.translate(-5,-5,-10)
vol.translate(-0.5*N, -0.5*N, -0.5*N)
w.addItem(vol)
#
# add an axis thingy
#ax = gl.GLAxisItem()
#w.addItem(ax)
if sys.flags.interactive != 1:
app.exec_()
def main():
# use standard notation
Nmu = 1.0
N = 20
mu = Nmu / float(N)
print 'N*mu:', Nmu
print 'N:', N
print
k = 4
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
nstates = len(M)
#R_mut = m_factor * wrightcore.create_mutation_collapsed(M, T)
#v = distn_helper(M, T, R_mut)
# get the approximations
alpha = 2*N*mu
approx_1a = get_beta_approx(N+1, alpha)
approx_2a = get_beta_approx(N+1, 2*alpha)
d4_reduction, d4_nstates = get_d4_reduction(M, T)
# for the initial guess all logs of ratios of probs are zero
x0 = np.zeros(d4_nstates - 1)
# precompute some design matrices
X_side = get_design_matrix_side(M)
X_diag = get_design_matrix_diag(M)
print 'number of variables:', d4_nstates - 1
print
f_errors = functools.partial(
eval_f,
M, T, d4_reduction, d4_nstates, approx_1a, approx_2a,
X_side, X_diag,
)
g_errors = functools.partial(eval_grad, f_errors)
f = functools.partial(apply_sum_of_squares, f_errors)
g = functools.partial(eval_grad, f)
h = functools.partial(eval_hess, f)
g_reverse = functools.partial(eval_grad_reverse_mode, f)
"""
result = scipy.optimize.leastsq(
f_errors,
x0,
Dfun=g_errors,
full_output=1,
)
"""
"""
result = scipy.optimize.fmin_ncg(
f,
x0,
fprime=g,
fhess=h,
avextol=1e-6,
full_output=True,
)
"""
result = scipy.optimize.fmin_bfgs(
f,
x0,
#fprime=g,
fprime=g_reverse,
full_output=True,
)
print result
xopt = result[0]
v = unpack_distribution(nstates, d4_reduction, d4_nstates, xopt)
# print some variances
check_variance(M, T, v)
def get_collapsed_diag_process_distn(m_factor, N, distn_helper):
k = 2
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
R_mut = m_factor * wrightcore.create_mutation_collapsed_diag(M, T)
return distn_helper(M, T, R_mut)
