def param_grid(feat_selec, mod_type, hyper_selc, rand_search_iter):
# Look into making PCA, Kbest and l1 dynamic
feat_param = {
'PCA': {'PCA__n_components': [5, 20, 300] if hyper_selc == 'Grid' else unint(5, 30, rand_search_iter)},
'KBest': {'KBest__k': [5, 20, 30] if hyper_selc == 'Grid' else unint(5, 30, rand_search_iter)},
'L1': {'L1__max_features': [5, 20, 30] if hyper_selc == 'Grid' else unint(5, 30, rand_search_iter)}}
mod_param = {'gbc': {'gbc__learning_rate': [0.1, 0.005, 0.001],
'gbc__n_estimators': [50, 100, 200],
'gbc__min_samples_split': [0.1, 0.3, 0.6, 0.8]},
'rf': {'rf__max_depth': [5, 8, 12, 15, 25] if hyper_selc == 'Grid' else unint(4, 25, rand_search_iter),
'rf__n_estimators': [30, 50, 80, 100, 130] if hyper_selc == 'Grid' else unint(30, 150,
rand_search_iter),
'rf__max_features': [0.2, 0.5, 0.8] if hyper_selc == 'Grid' else roundnp(
uni(0.1, 0, rand_search_iter)),
'rf__min_samples_split': [2, 8, 10, 15, 20, 30] if hyper_selc == 'Grid' else unint(2, 30,
rand_search_iter)},
'lr': {'lr__penalty': ['l2', 'l1', 'elasticnet'],
'lr__alpha': [0.0001, 0.0002, 0.0005],
'lr__max_iter': [3, 5, 7, 9]},
'svm': {'svm__C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000] if hyper_selc == 'Grid' else roundnp(
uni(0.0001, 1000, rand_search_iter)),
'svm__gamma': [0.01, 0.1, 1, 10, 100] if hyper_selc == 'Grid' else roundnp(
uni(0.0001, 1000, rand_search_iter)),
'svm__kernel': ['linear', 'poly', 'rbf']}
}
final_parm = feat_param[feat_selec]
final_parm.update(mod_param[mod_type])
return final_parm
def delta_EF_asym(mu, e, comp, f, p, alpha=None, max_ave_H=1):
"""computes the EF with asymptotic f, f(N) = f_i*H_i*N_i/(N_i+H_i)
For more information see S10
H_i is uniformly distributed in [0,2*ave_H]
Input
mu, e, comp, f, p:
As in output of rand_par
alpha: optional
Is not needed. They are just used s.t. one can
run delta_EF_asym
max_ave_H: scalar, optional
maximum for the average of H, maximum over all communities
returns:
deltaEF/EF: array
Array containing 100*deltaEF/EF, asymptotic contribution to EF"""
num = len(alpha) #number of species
# choose distributions of H: H ~u[0,2*ave]
temp = uni(0, max_ave_H, 3)
gam = {'avb': temp[0], 'avu': temp[1], 'avc': temp[2]}
temp = uni(-1 / sqrt, 1 / sqrt, 3)
gam.update({'tb': temp[0], 'tu': temp[1], 'tc': temp[2]})
H = lambda x,t: gam['av'+t]*(1+gam['t'+t]*sqrt*x)\
*mu['av'+t]*(1+mu['t'+t]*x*sqrt)
#asymptotic EF in N, f(N) = f_i*H_i*N_i/(N_i+H_i)
#change to consider different contribution to function
eco_fun = lambda x,t, N: f['av'+t]*(1+f['t'+t]*x*sqrt)*H(x,t)*N(x,t)\
/(N(x,t)+H(x,t))
# growthrates in different sites
mu_ref = lambda x, t: mu['av' + t] * (1 + x * sqrt * mu['t' + t])
mu_change = lambda x,t: mu['av'+t]*(1+x*sqrt*mu['t'+t])*\
(1-e['av'+t]*(1+e['t'+t]*sqrt*x))
# computes the equilibrium densities of species N, in changed and ref site
N = lambda x, t, mu, avmu: (mu(x, t) - comp * avmu) / (1 + alpha)
N_ref = lambda x, t: N(x, t, mu_ref, p * mu['avb'] + (1 - p) * mu['avu'])
N_change = lambda x, t: N(x, t, mu_change, mu['av_change'])
# integrate over all species for EF
x_simp = np.array(num * [np.linspace(-1, 1, 51)]) #x_axes
y_ref = {
'b': eco_fun(x_simp.T, 'b', N_ref).T,
'u': eco_fun(x_simp.T, 'u', N_ref).T
} #y_values in ref
y_cha = {
'b': eco_fun(x_simp.T, 'b', N_change).T,
'c': eco_fun(x_simp.T, 'c', N_change).T
} #y_values in change
# compute the EF
EF_ref = n * (p * simps(y_ref['b'], x_simp) +
(1 - p) * simps(y_ref['u'], x_simp))
EF_change = n * (p * simps(y_cha['b'], x_simp) +
(1 - p) * simps(y_cha['c'], x_simp))
return 100 * (EF_change - EF_ref) / EF_ref #multiply by 100 for percent
def experiment_n(start, end, step, trials, eps, c, m, sigma):
upper_bound = end + step
error = np.zeros(shape=(int((end - start) / step) + 1, 4, trials))
indices = np.zeros(shape=((upper_bound - start) / step, 1))
for n in np.arange(start, upper_bound, step):
print n
indices[(n - start) / step] = n
row = int((n - start) / step)
n0 = int(n * c)
n1 = int(n * (1 - c))
for i in range(trials):
l = uni(high=m)
data = nor(loc=l, scale=m / 2.0, size=n)
tcm_data = data[:n0]
loc_data = data[n0:]
mean = np.mean(data)
full_loc_err1 = (mean - lapLM(data, eps, m))**2
error[row][0][i] += full_loc_err1
l = uni(high=m)
data = nor(loc=l, scale=m / 6.0, size=n)
tcm_data = data[:n0]
loc_data = data[n0:]
mean = np.mean(data)
full_loc_err2 = (mean - lapLM(data, eps, m))**2
error[row][1][i] += full_loc_err2
l = uni(high=m)
data = nor(loc=l, scale=m / 10.0, size=n)
tcm_data = data[:n0]
loc_data = data[n0:]
mean = np.mean(data)
full_loc_err3 = (mean - lapLM(data, eps, m))**2
error[row][2][i] += full_loc_err3
full_loc_ana = fullLM_err(eps, m, n)
error[row][3][i] += full_loc_ana
error = np.mean(error, axis=2)
error = np.sqrt(error)
error = np.hstack((indices, error))
# error = np.log10(error)
print error
header = "n, k = 2, k = 6, k = 10, analyt"
np.savetxt("fullLM_plot.csv", error, header=header, delimiter=",")
def Thermalize(
): # function to calculate number of collisions required to thermalize.
NumCol = 0
E_n = StartingE
while E_n > ThermalEnergy:
NumCol += 1
E_loss_factor = uni(alpha, 1)
E_n = E_n * E_loss_factor
return NumCol
def experiment_n(start, end, step, trials, eps, c, m, sigma):
upper_bound = end + step
error = np.zeros(shape=(int((end - start) / step) + 1, 3, trials))
indices = np.zeros(shape=((upper_bound - start) / step, 1))
for n in np.arange(start, upper_bound, step):
print n
c = np.log(n) / n
indices[(n - start) / step] = n
row = int((n - start) / step)
n0 = int(n * c)
n1 = int(n * (1 - c))
for i in range(trials):
l = uni(high=m)
data = nor(loc=l, scale=sigma, size=n)
data = m * (data / float(max(data)))
# data = truncate(data, 0.0, 1.0)
tcm_data = data[:n0]
loc_data = data[n0:]
mean = np.mean(data)
only_tcm_err = (mean - tcm(tcm_data, eps, m))**2
error[row][0][i] = only_tcm_err
full_loc_err = (mean - lapLM(data, eps, m))**2
error[row][1][i] += full_loc_err
top_coeff = (c**2) * n
top_par_first = (eps**2) * (sigma**2)
top_par_sec = 2 * (m**2)
bot_first = c * n * (eps**2) * (sigma**2)
bot_coeff = 2 * (m**2)
bot_par = ((c**2) * n) + 1 - c
top = top_coeff * (top_par_first + top_par_sec)
bot = bot_first + bot_coeff * bot_par
w1 = top / bot
hybrid_err2 = (mean - hybrid(tcm_data, loc_data, eps, m, c, w1))**2
error[row][2][i] += hybrid_err2
error = np.mean(error, axis=2)
error = np.hstack((indices, error))
# error = np.log10(error)
print error
header = "n, OnlyTCM, FullLM, Hybrid"
np.savetxt("squared_error_" + str(eps) + "_" + str(c) + ".csv",
error,
header=header,
delimiter=",")
def delta_EF_asym(ave, t_e, t_mu, comp, t_f, n, alpha=None, max_ave_H=1):
"""computes the EF with asymptotic f, f(N) = f_i*H_i*N_i/(N_i+H_i)
For more information see S10
H_i is uniformly distributed in [0,2*ave_H]
Input
ave, t_e, t_mu, t_f, comp,n:
As in output of rand_par
alpha: optional
Is not needed. They are just used s.t. one can
run delta_EF_asym
returns:
deltaEF/EF: array
Array containing 100*deltaEF/EF"""
num = len(ave) #number of communities
# choose distribution of H: H ~u[0,2*ave]
ave_H = uni(0, max_ave_H, num)
t_H = uni(-1 / sqrt, 1 / sqrt, num) #stdv/mean of H
H = lambda x: ave_H * (1 + t_H * sqrt * x
) #H_i for each species in a community
#asymptotic EF in N, EF(N) = f_i*H_i*N_i/(N_i+H_i)
#change to consider different contribution to function
eco_fun = lambda x, N: n * (1 + t_f * x * sqrt) * H(x) * N(x) / (N(x) + H(
x))
# computes the equilibrium densities of species N, in changed and ref site
N_ref = lambda x: (1 + t_mu * sqrt * x - comp) / (1 + alpha)
N_change = lambda x: ((1+x*t_mu*sqrt)*(1-ave*(1+t_e*sqrt*x))-\
comp*(1-ave*(1+t_mu*t_e)))/(1+alpha)
# integrate over all species for EF
x_simp = np.array(num * [np.linspace(-1, 1, 51)]) #x_axes
y_ref = eco_fun(x_simp.T, N_ref).T #y_values in ref
y_change = eco_fun(x_simp.T, N_change).T #y values in changed site
EF_ref = simps(y_ref, x_simp)
EF_change = simps(y_change, x_simp)
return 100 * (EF_change - EF_ref) / EF_ref #multiply by 100 for percent
def _update_next_time(self, time):
# Invalidate the current entry
self._invalidate_queue_entry()
# Calculate the reaction propensity
p = self.rate
for m, s in self.participants:
N = s.number
for n in range(m):
p *= N - n
# Update the queue with the next reaction time only if the reaction
# actually occurs
if p > 0.:
self.next_time = time - log(uni()) / p
self._update_queue()
def _update_next_time(self, time):
# Invalidate the current entry
self._invalidate_queue_entry()
# Calculate the reaction propensity
p = self.rate
for m, s in self.participants:
N = s.number
for n in range(m):
p *= N - n
# Update the queue with the next reaction time only if the reaction
# actually occurs
if p > 0.0:
self.next_time = time - log(uni()) / p
self._update_queue()
def hybrid_online(tcm_data, loc_data, n, eps, m, c):
tcm_mean = tcm(tcm_data, c * n, eps, m)
run_mean = tcm_mean
ptrue = np.exp(eps) / (np.exp(eps) + 1)
probs = uni(size=(1 - c) * n)
for i in range(len(loc_data)):
if loc_data[i] < run_mean:
if probs[i] < ptrue:
run_mean += m / (c * n + i)
else:
run_mean -= m / (c * n + i)
if loc_data[i] > run_mean:
if probs[i] < ptrue:
run_mean -= m / (c * n + i)
else:
run_mean += m / (c * n + i)
# run_mean = truncate(run_mean, 0.0, 1.0)
return run_mean
def __init__(self, robot: Robot):
self.robot: Robot = robot
# STATE MU
self.mu = numpy.matrix(
[[self.robot.x], [self.robot.y], [self.robot.theta]],
dtype='float')
self.mu_prediction = self.mu.copy()
# MOTION MODEL VALUES
self.u = numpy.matrix([[self.robot.v], [self.robot.w]], dtype='float')
# STATE COVARIANCE ESTIMATE
self.sigma = numpy.diag(
(SETTINGS["INITIAL_COVARIANCE"], SETTINGS["INITIAL_COVARIANCE"],
SETTINGS["INITIAL_COVARIANCE"]))
self.sigma_prediction = self.sigma.copy()
# UNCONTROLLED TRANSITION MATRIX A
self.A = numpy.identity(3)
# CONTROL TRANSITION MATRIX B
self.B = numpy.matrix([[Robot.DELTA_T * math.cos(self.robot.theta), 0],
[Robot.DELTA_T * math.sin(self.robot.theta), 0],
[0, Robot.DELTA_T]],
dtype='float')
# MOTION NOISE ESTIMATION
self.R = numpy.matrix(
[[uni(0, SETTINGS["MOTION_NOISE_ESTIMATION"]), 0, 0],
[0, uni(0, SETTINGS["MOTION_NOISE_ESTIMATION"]), 0],
[0, 0, uni(0, SETTINGS["MOTION_NOISE_ESTIMATION"])]],
dtype='float')
# MAPPING STATES TO OBSERVATIONS
self.C = numpy.identity(3)
# IDENTITY MATRIX
self.I = numpy.identity(3)
# SENSOR NOISE ESTIMATION
self.Q = numpy.matrix(
[[uni(0, SETTINGS["SENSOR_NOISE_ESTIMATION"]), 0, 0],
[0, uni(0, SETTINGS["SENSOR_NOISE_ESTIMATION"]), 0],
[0, 0, uni(0, SETTINGS["SENSOR_NOISE_ESTIMATION"])]],
dtype='float')
# STATE ESTIMATED FROM SENSOR DATA
self.z = numpy.zeros((3, 1))
# KALMAN GAIN
self.K_trace = [0]
def spawn_random_tiles(tiles):
dimension = len(tiles)
empty_tiles = []
for i in range(dimension):
for j in range(dimension):
if not tiles[i][j]:
empty_tiles.append((i, j))
if len(empty_tiles) == 0:
return None
global number_of_spawns
for i in range(min(number_of_spawns, len(empty_tiles))):
rand_index = randint(len(empty_tiles))
i, j = empty_tiles.pop(rand_index)
# set value 2 or 4
tiles[i][j] = 2 if uni() < 0.9 else 4
return tiles
def fit(confirmed0, death0, reopen_day_gov, n_0):
np.random.seed()
confirmed = confirmed0.copy()
death = death0.copy()
size = len(confirmed)
# if metric2 != 0 or metric1 != 0:
# scale1 = pd.Series(np.random.normal(1, metric1, size))
# confirmed = [max(confirmed[i] * scale1[i], 1) for i in range(size)]
# scale2 = pd.Series(np.random.normal(1, metric2, size))
# death = [max(death[i] * scale2[i], 1) for i in range(size)]
c_max = 0
min_loss = 10000
for reopen_day in range(reopen_day_gov, reopen_day_gov + 14):
for c1 in np.arange(c1_range[0], c1_range[1], 0.01):
# optimal = minimize(loss, [10, 0.05, 0.01, 0.1, 0.1, 0.1, 0.02], args=(c1, confirmed, death, n_0, SIDRG_sd),
optimal = minimize(loss_combined, [uni(beta_range[0], beta_range[1]),
uni(gammaE_range[0], gammaE_range[1]),
uni(alpha_range[0], alpha_range[1]),
uni(gamma_range[0], gamma_range[1]),
uni(gamma2_range[0], gamma2_range[1]),
uni(gamma3_range[0], gamma3_range[1]),
uni(a1_range[0], a1_range[1]),
uni(a2_range[0], a2_range[1]),
uni(a3_range[0], a3_range[1]),
uni(eta_range[0], eta_range[1]),
uni(h_range[0], h_range[1]),
uni(Hiding_init_range[0], Hiding_init_range[1]),
uni(I_initial_range[0], I_initial_range[1])],
args=(c1, confirmed, death, n_0, reopen_day), method='L-BFGS-B',
bounds=[beta_range,
gammaE_range,
alpha_range,
gamma_range,
gamma2_range,
gamma3_range,
a1_range,
a2_range,
a3_range,
eta_range,
h_range,
Hiding_init_range,
I_initial_range])
current_loss = loss_combined(optimal.x, c1, confirmed, death, n_0, reopen_day)
if current_loss < min_loss:
# print(f'updating loss={current_loss} with c1={c1}')
min_loss = current_loss
c_max = c1
reopen_max = reopen_day
beta = optimal.x[0]
gammaE = optimal.x[1]
alpha = optimal.x[2]
gamma = optimal.x[3]
gamma2 = optimal.x[4]
gamma3 = optimal.x[5]
a1 = optimal.x[6]
a2 = optimal.x[7]
a3 = optimal.x[8]
eta = optimal.x[9]
h = optimal.x[10]
Hiding_init = optimal.x[11]
I_initial = optimal.x[12]
c1 = c_max
reopen_day = reopen_max
S = [n_0 * eta * (1 - Hiding_init)]
E = [0]
I = [n_0 * eta * I_initial * (1 - alpha)]
A = [n_0 * eta * I_initial * alpha]
IH = [0]
IN = [I[-1] * gamma2]
D = [death[0]]
R = [0]
G = [confirmed[0]]
H = [n_0 * eta * Hiding_init]
# H = [0]
# Betas = [beta]
result, [S, E, I, A, IH, IN, D, R, G, H, betas] \
= simulate_combined(size, S, E, I, A, IH, IN, D, R, G, H, beta, gammaE, alpha, gamma, gamma2, gamma3, a1, a2,
a3, h, Hiding_init, eta, c1, n_0, reopen_day)
# data1 = [(confirmed[i] - G[i]) / confirmed[i] for i in range(size)]
# data2 = [(death[i] - D[i]) / death[i] for i in range(size)]
size1 = reopen_day
size2 = size - size1
weights1 = [Geo ** n for n in range(size1)]
weights1.reverse()
weights2 = [Geo ** n for n in range(size2)]
weights2.reverse()
weights = weights1
weights.extend(weights2)
# weights = [Geo ** n for n in range(size)]
# weights.reverse()
# sum_wt = sum(weights)
# metric1 = math.sqrt(sum([data1[i] ** 2 * weights[i] for i in range(size)])
# /
# ((size - 12) * sum_wt / size)
# )
# metric2 = math.sqrt(sum([data2[i] ** 2 * weights[i] for i in range(size)])
# /
# ((size - 12) * sum_wt / size)
# )
metric1 = weighted_relative_deviation(weights, confirmed, G, start_dev, num_para)
metric2 = weighted_relative_deviation(weights, death, D, start_dev, num_para)
r1 = r2_score(confirmed, G)
r2 = r2_score(death, D)
return [beta, gammaE, alpha, gamma, gamma2, gamma3, a1, a2, a3, eta, h, Hiding_init, c1, I_initial, metric1,
metric2, r1, r2, reopen_day], min_loss
def initialize(param_dict:dict=None):
"""
Set up initial strains and phages
Return: com (Community)
"""
from Enums import Type
from numpy.random import uniform as uni, randint
if not param_dict is None:
param_dict = dict() # will contain parameters already specified at the command line
for arg in params:
if arg in globals() and not globals()[arg] is None:
param_dict[arg] = globals()[arg]
# for arg,val in globals().items():
# if arg in params and not val is None:
# param_dict[arg] = val
# if parameters aren't specified, draw them from distributions
pS = param_dict.get( "pS", 10**-( uni(6,9) ) ) # default: random float 10^-5 - 10^-9
b = param_dict.get( "b", uni(0.9, 2) )
a = param_dict.get( "a", 10**( uni(5,8) ) )
c = param_dict.get( "c", 10**-( uni(1,3) ) )
f = param_dict.get( "f", 10**-( uni(5,7) ) )
beta = param_dict.get( "beta", randint(50,200) ) # default: random int from 1-200
adsp = param_dict.get( "adsp", 10**-( uni(7,9) ) )
d = param_dict.get( "d", uni(0.0, 0.3) )
m = param_dict.get( "m", 10**-( uni(6,9) ) )
l = param_dict.get( "l", uni(0.0, 1.0) )
popinit = param_dict.get( "popinit", 10**( uni(5,7) ) )
phageinit = param_dict.get( "phageinit", 10**( uni(5,7) ))
receptor1 = PhageReceptor.PhageReceptor( name = "r1" ) # change numbering system
crispr0 = Crispr.Crispr()
strain1 = Strain.Strain(
name = "s0001",
type_ = 'init',
a=a,b=b,c=c,y=y,f=f,pS=pS,
crispr = crispr0,
phReceptors = {
receptor1.name:receptor1
},
pop = popinit,
evoTraits=strain_evolved
)
# strain2 = Strain.Strain(
# name = "s2",
# a=a,b=b,c=c,y=y,f=f,
# crispr = crispr0,
# phReceptors = {
# receptor1.name:receptor1
# },
# pop = popinit/100
# )
nameGenerator = gen.NameGenerator()
protospacers = set()
for i in range(8):
protospacers.add( nameGenerator.generateName(Type.PROTO))
phage1 = Phage.Phage(
name = "p0001",
adsp = adsp,beta = beta, d = d, m = m,
receptor = receptor1,
pop = phageinit,
protospacers = protospacers,
evoTraits=phage_evolved
)
phage2 = Phage.Phage(
name = "p2",
adsp = adsp,beta = beta, d = d, m = m*10,
receptor = receptor1,
pop = phageinit,
protospacers = protospacers,
evoTraits=phage_evolved
)
# spacer = strain2.crispr.makeSpacer("AGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGT")
# spacer = strain2.crispr.makeSpacer(phage1.genome)
# strain2.crispr.addSpacer(spacer)
# pop1 = Population.Population(
# name = "pop1",
# strains = {
# strain1.name: strain1
# }
# )
com = Community.Community(
c=c,l=l,
strains = {
strain1.name: strain1,
# strain2.name: strain2
},
phages = {
phage1.name: phage1,
phage2.name: phage2
},
nameGenerator=nameGenerator,
evoTraits=evolved_params,
le = 0.2
)
com.summary = pd.Series(
data = {
"id": uuid.uuid1(),
"pop":np.nan,
"phage":np.nan,
"immune":np.nan,
"susceptible":np.nan,
"richness":np.nan,
"phageRichness":np.nan,
"pS":pS,
"b":b,
"a":a,
"c":c,
"f":f,
"beta":beta,
"adsp":adsp,
"d":d,
"m":m,
"l":l,
"popinit":popinit,
"phageinit":phageinit,
"nodf":np.nan,
"Q":np.nan,
}
)
return com
def rand_par(e_min=-1,
ave_min=-0.5,
ave_max=0.5,
e_max=1,
p='rand',
ad_com=0.005,
num=100000):
""" returns randomized parameters for num_com communities
The function randomly generates num_com*(1+ad_com) communities until it
finds num_com communities fullfilling all coexistence requirements. This
method slightly shifts the distribution of alpha and e towards 0. The
error in the distribution is smaller than ad_com
Pleasse refer to supplementary data 7 to understand the code
Input:
num_com: scalar
number of species to be generated
p: scalar or string
Percent of species that are in ref and changed site (Type b species)
p*n must be an integer or p ='rand'
'rand will randomly generate values for p
Note: p = 1 is NOT equivalent to coex.rand_par, because of the
coexistence requirements.
ave_max, ave_min: scalar<1, ave_min<=ave_max
the maximum/minimum that are allowed for the
average sensitivity of each species type
e_max, e_min: scalar<1, e_min<=ave_min,ave_max<=e_max
the maximum/minimum that are allowed for the sensitivity
of each species (individually)
ad_com: scalar
Proportion of additionally computed communities.
returns:
mu: dict
Contains all growth rates and some associated values
e: dict
contains the sensitivity
comp: array
Relative competition
f: dict
contains the per capita contributions
p_ret: array
Percent of species that are in ref and changed site (Type b species)
if p was a scalar, then p_ret = p*np.ones(num_com).
alpha: array
competition parameter
"""
#check input correctness
if not (e_min <= ave_min <= ave_max <= e_max):
raise InputError(
"Please sort the input: e_min<=ave_min<=ave_max<=e_max")
if e_max > 1: #growth rate of that species would be negative
raise InputError("e_max>1, effects above 1 are not allowed")
if not (p == 'rand'
or p * n == int(p * n)): #a species goes extinct or not
raise InputError("p must either be 'rand' or p*n must be an integer")
#save the original number of communites
num_com = num
#number of communities to construct
num = int(np.ceil(num_com * (1 + ad_com)))
# fixed parameters, do not change to find communities
e_fix = {
'avb': dist(ave_min, ave_max, num), #average effect on species b
'avc': np.zeros(num), #will be filled with data while running
'tc': np.zeros(num),
'tb': np.zeros(num)
}
mu_fix = {
'avb': np.zeros(num), #will be filled with data while running
'avc': np.zeros(num),
'tc': np.zeros(num),
'avu': np.zeros(num),
'tu': np.zeros(num),
'tb': np.zeros(num)
}
alpha = uni(-0.95, -0.05, num) # interaction coecfficient
comp_fix = -alpha * n / (1 - alpha *
(n - 1)) #effective competition , computed
# Fixed communities fullfill coexistence requirements
not_fix = np.array(num * [True])
# percent of species with type C
if p is 'rand':
p_fix = np.random.randint(1, n - 1, num) / n
else:
p_fix = p * np.ones(num)
#randomly generate communities, until num_com many fullfill coex. req.
#attention, changing settings might turn this into an infinite loop
while num > num_com * ad_com:
#copy the predefined values into arrays to be used
e = {'avb': e_fix['avb'][not_fix]}
p = p_fix[not_fix]
q = 1 - p
comp = comp_fix[not_fix]
# min(mu['avb'],mu['avu'])/(p*mu['avb']+q*mu['avu'])>comp
mu = {'avb': dist(0, 10, num)}
mu['avu'] = dist(
mu['avb'] * comp * p / (1 - q * comp),
np.amin(
[mu['avb'] * (1 - p * comp) / (q * comp), 10 * np.ones(num)],
axis=0))
#coexistence limit, min(mu)/mean(mu)>comp
tresh1 = comp * (p * mu['avb'] + q * mu['avu'])
# chosen such that min (mu_u,mu_b) > tresh1, i.e. coexist
mu['tb'] = dist(-(1 - tresh1 / mu['avb']) / sqrt,
(1 - tresh1 / mu['avb']) / sqrt)
mu['tu'] = dist(-(1 - tresh1 / mu['avu']) / sqrt,
(1 - tresh1 / mu['avu']) / sqrt)
# mu['avc']*(1-ave_min) must be able to coexist in changed site
tresh2 = mu['avb'] * (1 - e['avb']) * p * comp / (1 - comp * q) / (
1 - ave_min)
# we always have treshhold2<treshhold1
mu['avc'] = dist(tresh2, tresh1)
# ensure, that min(mu_c) fullfills same conditions as mu['avc']
bound = np.amin([tresh1 / mu['avc'] - 1, 1 - tresh2 / mu['avc']],
axis=0) / sqrt
mu['tc'] = dist(-bound, bound)
# mu['avc']*(1-e['avc']) fullfills coexistence conditions
# choose min for e['avc']
tresh1 = np.amax([1-mu['avb']/mu['avc']*(1-e['avb'])*(1-comp*p)\
/(q*comp),ave_min*np.ones(num)],axis = 0)
# choose max for e['avc']
tresh2 = np.amin([1-mu['avb']/mu['avc']*(1-e['avb'])/(1-comp*q)\
*(p*comp),ave_max*np.ones(num)],axis = 0)
e['avc'] = dist(tresh1, tresh2)
# choose borders, that e_i are within [e_min, e_max]
minimum = np.amin([
np.sign(e['avb']) * (e_max / e['avb'] - 1),
np.sign(e['avb']) * (1 - e_min / e['avb'])
],
axis=0)
e['tb'] = uni(-minimum / sqrt, minimum / sqrt)
minimum = np.amin([
np.sign(e['avc']) * (e_max / e['avc'] - 1),
np.sign(e['avc']) * (1 - e_min / e['avc'])
],
axis=0)
e['tc'] = dist(-minimum / sqrt, minimum / sqrt)
# average growthsrates in changed site of the species types
mu['avb_change'] = mu['avb'] * e['avb'] * (1 / e['avb'] - 1 -
mu['tb'] * e['tb'])
mu['avc_change'] = mu['avc'] * e['avc'] * (1 / e['avc'] - 1 -
mu['tc'] * e['tc'])
# average growthrate of entire community in changed site
mu['av_change'] = p * mu['avb_change'] + q * mu['avc_change']
# reference types are assumed to have e_i = 1, always
# if this part of the code is changed, please also change in coex_test
# e['avu'] = 1 #change if desired differently
# e['tu'] = 0
#copy the parameters into the fixed parameters
for k in e_fix.keys():
if k == 'avb': #do not copy into fixed 'avb'
pass
e_fix[k][not_fix] = e[k]
for k in mu_fix.keys():
mu_fix[k][not_fix] = mu[k]
#check which species can coexist and update not_fix
coex = coex_test(mu, e, comp)
not_fix[not_fix] = np.logical_not(coex)
num = np.count_nonzero(not_fix) #number of not fixed communities
fix = np.logical_not(not_fix) #communities that are fixed, i.e. coex
# choose only num_com coexisting communities
comp_ret = comp_fix[fix][:num_com]
alpha_ret = alpha[fix][:num_com]
p_ret = p_fix[fix][:num_com]
mu_ret = {key: mu_fix[key][fix][:num_com] for key in mu_fix.keys()}
e_ret = {key: e_fix[key][fix][:num_com] for key in e_fix.keys()}
# average growthsrates in changed site of the species types
mu_ret['avb_change'] = mu_ret['avb']*e_ret['avb']*\
(1/e_ret['avb']-1 - mu_ret['tb']*e_ret['tb'])
mu_ret['avc_change'] = mu_ret['avc']*e_ret['avc']*\
(1/e_ret['avc']-1 - mu_ret['tc']*e_ret['tc'])
# average growthrate of entire community
mu_ret['av_change'] = p_ret*mu_ret['avb_change']+\
(1-p_ret)*mu_ret['avc_change']
# generate distribution of per capita contributions for species types
t_fb, t_fu, t_fc = uni(-1 / sqrt, 1 / sqrt, [3, num_com]) # stdv/mean
avfb, avfu, avfc = uni(0.5, 1.5, [3, num_com]) #averages of f
f = {'avb':avfb,'avu':avfu,'avc':avfc,\
'tb':t_fb,'tu':t_fu,'tc':t_fc}
# communities fullfill coexistence
return mu_ret, e_ret, comp_ret, f, p_ret, alpha_ret
from numpy.random import uniform as uni
qual = 1000000
result = 0
for i in range(qual):
x = uni(-3, 3)
y = uni(-3, 3)
z = uni(-3, 3)
if x**2 + y**2 < 9 and z**2 + y**2 < 9:
result += 1
print(6**3 * result / qual)
actual = [30.8,14.8,3.7, 6.0,3.0,0.8]
simulated = map(lambda x: 100*x, GraphsAndData.number_of_partners_data(s, year = s.NUMBER_OF_YEARS-0))
sexual_partners += sum([abs(actual[i] - simulated[i]) for i in range(len(actual))])
#return result of test
return age_disparate + sexual_partners
#MPI variables
name = MPI.Get_processor_name()
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
#%% 0. Setup
# Every prior distribution is a uniform and given by a bottom and top
prior = {
1: lambda: uni(-0.01, -0.5), # probability multiplier
2: lambda: uni(-0.01, -0.5), # preferred age difference
3: lambda: uni(0.01, 2), # preferred age difference growth
4: lambda: uni(1, 4), # DNP scale
5: lambda: uni(0.05, 0.9), # DNP shape
6: lambda: uni(15, 30), # durations scale
7: lambda: uni(0, 5), # durations shape
}
posterior = {i:[] for i in prior.keys()}
##file to write
##rows 0-7
#print "#,ProbMult, PAD, PADgrowth, DNPscale, DNPshape, DURAscale, DURAshape," # parameters
##rows 8-11, 12-15
#print "NonInterMale05,InterMale05,NonInterFemale05,InterFemale05," # intergernational sex 2005
r_step = sqrt((x_new - x_prev)**2 + (y_new - y_prev)**2)
NS = NS + 1
n = n + 1
else:
break
return x_new, y_new, NS
#Local_x,Local_y,NS=Localsearch(1,0,3)
'''solving with VNS'''
Ackley = []
X = []
Y = []
Time = []
for j in range(20):
x, y = uni(-32.768, 32.768), uni(-32.768, 32.768)
start_time = time.time()
for i in range(1000):
for r in (0.1, 0.5, 3, 5, 8, 13, 21, 34):
j = True
while j:
a = neighborpoints(x, y, r)
b = Localsearch(a[0], a[1])
if ackley(b[0], b[1]) < ackley(x, y):
x, y = b[0], b[1]
else:
j = False
end_time = time.time()
Ackley.append(ackley(x, y))
X.append(x)
Y.append(y)
