I have currently tried out multiple switchpoints using a list of parameters and theano switch function. How would I do this in a better way without this ugly lists.
with model:
switchpoints = []
mus = []
sds = []
for point in range(num_switch):
if point == 0:
switchpoints.append(pm.DiscreteUniform("switchpoint{0}".format(point),
lower=indices[0], upper=indices[-1]))
else:
switchpoints.append(pm.DiscreteUniform("switchpoint{0}".format(point),
lower=switchpoints[point - 1], upper=indices[-1]))
for point in range(num_switch + 1):
mus.append(pm.Exponential("mu_{0}".format(point), alpha))
sds.append(pm.Exponential("sd_{0}".format(point), beta))
tau_mus = []
tau_sds = []
for point in range(num_switch):
if point == 0:
tau_mus.append(pm.math.switch(switchpoints[point] >= indices, mus[0], mus[1]))
tau_sds.append(pm.math.switch(switchpoints[point] >= indices, sds[0], sds[1]))
else:
tau_mus.append(pm.math.switch(switchpoints[point] >= indices, tau_mus[point - 1], mus[point]))
tau_sds.append(pm.math.switch(switchpoints[point] >= indices, tau_sds[point - 1], sds[point]))
likelihood = pm.Normal('likelihood', mu=tau_mus[-1], sd=tau_sds[-1], observed=self.data)
step1 = pm.Metropolis()
trace = pm.sample(self.number_samples, step=step1)
Related
I am trying to get all solutions for a Mixed Integer program through ortools. I have two lists x and y of size 4. I want to get all solutions which satisfy sum(x) = 4 * sum(y). I created a function which takes list of past solutions as input and returns next solution. I am able to get only 2 solutions even though there are more. What am I doing wrong here?
I am expecting the following solutions
Solution 1:
xs1 = [0,0,0,0], ys1 = [0,0,0,0]
Solution 2:
xs2 = [4,0,0,0], ys2 = [1,0,0,0]
Solution 3:
xs3 = [0,4,0,0], ys3 = [1,0,0,0]
Solution 4:
xs4 = [0,0,4,0], ys4 = [0,0,1,0]
and soon on
from ortools.linear_solver import pywraplp
def opt(xs, ys):
solver = pywraplp.Solver.CreateSolver('SCIP')
infinity = solver.infinity()
# x and y are integer non-negative variables.
n = 4
M = 20
x = [0]* n
y = [0]* n
w = [[0]* n]*len(xs)
δ = [[0]* n]*len(xs)
for i in range(0,n):
x[i] = solver.IntVar(0, 20, 'x'+str(i))
y[i] = solver.IntVar(0, 20, 'y'+str(i))
for j in range(len(xs)):
w[j][i] = solver.IntVar(0, 20, 'zp'+str(j)+ '-' + str(i))
δ[j][i] = solver.IntVar(0, 1, 'δ'+str(j)+ '-' + str(i))
for j in (range(len(xs))):
for i in range(0,n):
solver.Add((w[j][i] - x[i] + xs[j][i]) >=0)
solver.Add((w[j][i] - x[i] + xs[j][i]) <= M*(1-δ[j][i]))
solver.Add((w[j][i] + x[i] - xs[j][i]) >=0)
solver.Add((w[j][i] + x[i] - xs[j][i]) <= M*δ[j][i])
for j in range(len(xs)):
solver.Add(solver.Sum([w[j][i] for i in range(0,n)]) >= 1)
solver.Add(solver.Sum([x[i] for i in range(0, n)]) - 4 * solver.Sum([y[i] for i in range(0, n)]) == 0)
solver.Minimize(solver.Sum([x[i] for i in range(0, n)]))
status = solver.Solve()
if status == pywraplp.Solver.OPTIMAL:
solver_x = [0]*n
solver_y = [0]*n
for i in range(0,n):
solver_x[i] = x[i].solution_value()
solver_y[i] = y[i].solution_value()
return ([solver_x, solver_y, solver.Objective().Value()])
else:
print('No Solution')
return ([[0], [0]], -1)
psx = [[0,0,0,0], [0,4,0,0]]
psy = [[0,0,0,0], [1,0,0,0]]
ns = opt(psx, psy)
print(ns)
Output:
No Solution
([[0], [0]], -1)
Reference:
Finding multiple solutions to general integer linear programs
How to write constraints for sum of absolutes
If you have a pure integer programming model, you can use the CP-SAT solver which allows you to print all the solutions [See this].
I'm trying to plot two files of data of this type:
name1.fits 0 0 2.40359218172
name2.fits 0 0 2.15961244263
The third column has values from 0 to 5. I want to plot column 2 vs column 4, but, for lines with values in col 3 less than 2 (0 and 1), I want to shift col 2 by -0.1, and for lines with values greater than 3 (4 and 5) I want to shift col 2 by +0.1.
However my code seems to be shifting all values by +0.1. Here is what I have so far:
import matplotlib.pyplot as plt
import numpy as np
with open('file1.txt') as data, open('file2.txt') as stds:
lines1 = data.readlines()
lines2 = stds.readlines()
x1a = []
x2a = []
x1b = []
x2b = []
x1c = []
x2c = []
y1a = []
y2a = []
y1b = []
y2b = []
y1c = []
y2c = []
for line1 in lines1:
p = line1.split()
if p[2] < 2:
x1a.append(float(p[1]))
y1a.append(float(p[3]))
elif 1 < p[2] < 4:
x1b.append(float(p[1]))
y1b.append(float(p[3]))
elif p[2] > 3:
x1c.append(float(p[1]))
y1c.append(float(p[3]))
for line2 in lines2:
q = line2.split()
if q[2] < 2:
x2a.append(float(q[1]))
y2a.append(float(q[3]))
elif 1 < q[2] < 4:
x2b.append(float(q[1]))
y2b.append(float(q[3]))
elif q[2] > 3:
x2c.append(float(q[1]))
y2c.append(float(q[3]))
x1a = np.array(x1a)
x2a = np.array(x2a)
x1b = np.array(x1b)
x2b = np.array(x2b)
x1c = np.array(x1c)
x2c = np.array(x2c)
y1a = np.array(y1a)
y2a = np.array(y2a)
y1b = np.array(y1b)
y2b = np.array(y2b)
y1c = np.array(y1c)
y2c = np.array(y2c)
minorLocator = AutoMinorLocator(5)
fig, ax = plt.subplots(figsize=(8, 8))
fig.subplots_adjust(left=0.11, right=0.95, top=0.94)
plt.plot(x1a-0.1,y1a,'b^',mec='blue',label=r'B0',ms=8)
plt.plot(x2a-0.1,y2a,'r^',mec='red',fillstyle='none',mew=0.8,ms=8)
plt.plot(x1b,y1b,'bo',mec='blue',label=r'B0',ms=8)
plt.plot(x2b,y2b,'ro',mec='red',fillstyle='none',mew=0.8,ms=8)
plt.plot(x1c+0.1,y1c,'bx',mec='blue',label=r'B0',ms=8)
plt.plot(x2c+0.1,y2c,'rx',mec='red',fillstyle='none',mew=0.8,ms=8)
plt.axis([-1.0, 3.0, 0., 4])
ax.xaxis.set_tick_params(labeltop='on')
ax.yaxis.set_minor_locator(minorLocator)
plt.show()
Here is the plot:
plot
I'm pretty sure the problem is in my "ifs". I hope you can clear the way and/or show me a better option for this.
When you do your queries (if) you must ensure the conversion happens before the question so:
for line1 in lines1:
p = line1.split()
if p[2] < 2:
x1a.append(float(p[1]))
y1a.append(float(p[3]))
elif 1 < p[2] < 4:
x1b.append(float(p[1]))
y1b.append(float(p[3]))
elif p[2] > 3:
x1c.append(float(p[1]))
y1c.append(float(p[3]))
, should actually be:
for line1 in lines1:
p = line1.split()
if float(p[2]) < 2: # changed here
x1a.append(float(p[1]))
y1a.append(float(p[3]))
elif 1 < float(p[2]) < 4: # There seems to be a problem with this if
x1b.append(float(p[1]))
y1b.append(float(p[3]))
elif float(p[2]) > 3: # changed here
x1c.append(float(p[1]))
y1c.append(float(p[3]))
The same for your q variables. Also notice that asking 1 < x < 4 will intercept with x > 3 and x < 2. You should also correct this.
I have a script as follows:
import numpy as np
import pandas as pd
import pdb
# conventions: W = fitness, A = affinity ; sex: 1=M, 0=F; alien: 1=alien,
# 0=native
# pop array order: W, A, sex, alien
def mkpop(n):
W = np.repeat(a=1, repeats=n)
A = np.random.normal(1, 0.1, size=n)
A[A < 0] = 0
alien = np.repeat(a=False, repeats=n)
sex = np.random.randint(0, 2, n)
pop = np.array([W, A, sex, alien])
pop = np.transpose(pop)
return pop
def migrate(pop, n=10, gParams=[1, 0.1]):
W = np.random.gamma(shape=gParams[0], scale=gParams[1], size=n)
A = np.repeat(1, n)
# 0 is native; 1 is alien
alien = np.repeat(True, n)
# 0 is female
sex = np.random.randint(0, 2, n)
popAlien = np.array([W, A, sex, alien])
popAlien = np.transpose(popAlien)
pop = np.vstack((pop, popAlien))
return pop
def mate(pop):
# split into male and female
f = pop[pop[:, 2] == 0]
m = pop[pop[:, 2] == 1]
# create transition matricies for native and alien mates
# m with native = m.!alien.transpose * f.alien
# negate alien
naLog = list(np.asarray(m[:, 3]) == False)
naPdMat = np.outer(naLog, f[:, 1])
# mate with alien = m.alien.transpose * affinity
alPdMat = np.outer(m[:, 3], f[:, 1])
# add transition matrices for probability density matrix
pdMat = alPdMat + naPdMat
# transition matrix is equal to the pd matrix / column sumso
colSums = np.sum(pdMat, axis=0)
pMat = pdMat / colSums
# select mates
def choice(x):
ch = np.random.choice(a=range(0, len(x)), p=x)
return ch
mCh = np.apply_along_axis(choice, 0, pMat)
mCh = m[mCh, :]
WMid = (f[:, 0] + mCh[:, 0]) / 2
AMid = (f[:, 1] + mCh[:, 1]) / 2
# assign fitness based on group affiliation; only native/alien matings have
# modified fitness
# reassign fitness and affinity based on group id and midparent vals
W1 = np.where(
(f[:, 3] == mCh[:, 3]) |
((f[:, 3] == 1) & (mCh[:, 3] == 0))
)
WMid[W1] = 1
# number of offspring is a poisson-distributed variable with lambda=2W
nOff = map(lambda x: np.random.poisson(lam=x), 2 * WMid)
# generate offspring
# expand list of nOff to numbers of offspring per pair
# realized offspring is index posisions of W and A vals to be replicated
# for offspring
# this can be rewritten to return a matrix of the appropriate length. This
# should work
midVals = np.array([WMid, AMid]).T
realOff = np.array([0, 0])
for i in range(0, len(nOff)):
sibs = np.repeat([np.array(midVals[i])], [nOff[i]], axis=0)
realOff = np.vstack((realOff, sibs))
offspring = np.delete(realOff, 0, 0)
sex = np.random.randint(0, 2, len(offspring))
alien = np.repeat(0, len(offspring))
otherStats = np.array([sex, alien]).T
offspring = np.hstack([offspring, otherStats])
return offspring # should return offspring
def sim(nInit, nGen=100, nAlien=10, gParams=[1, 0.1]):
gen = 0
pop = mkpop
stats = pd.DataFrame(columns=('gen', 'W', 'WMean', 'AMean', 'WVar', 'AVar'))
while gen < nGen:
pop = migrate(pop, nAlien, gParams)
offspring = mate(pop)
var = np.var(offspring, axis=0)
mean = np.mean(offspring, axis=0)
N = len(offspring)
W = N / nInit
genStats = N.append(W, gen, mean, var)
stats = stats.append(genStats)
print(N, gen)
gen = gen + 1
return stats
print mkpop(100)
print mate(mkpop(100))
#
sim(100, 100, 10, [1, 0.1])
Running this script, outputs NameError: name 'sim' is not defined. It is apparent from the commands before the final one that all the other functions defined within this script work without a hitch. I'm not sure what is going on here, and there is probably some very easy fix that I'm overlooking. Ctags recognizes this function just fine. It's entirely possibe that sim() doesn't actually work yet, as I haven't been able to debug it.
Your sim function defined in mate function scope so it's invisible to global scope. You need to fix your indentation for sim function
for merge sort i wrote this code:
I have tested merge function that works correctly. but in mergeSort function i coudn't handle the arrays. it returns the same list as the input list.
def mergeSort(a):
l, h = 0, len(a)-1
mid = (l+h)/2
if (l<h-1): #the lowest length must be 2
mergeSort(a[l:mid+1])
mergeSort(a[mid+1:h+1])
return merge(a[l:mid+1],a[mid+1:h+1])
def merge(a,b):
n_a = len(a)
n_b = len(b)
c = [[] for i in range(n_a + n_b)]
i,j,k=0,0,0
while (i<n_a and j<n_b):
if a[i]<b[j]:
c[k] = a[i]
i += 1
else:
c[k]= b[j]
j += 1
k += 1
while(i<n_a):
c[k] = a[i]
k+=1
i+=1
while(j< n_b):
c[k] = b[j]
k+=1
j+=1
return c
I would rewrite merge as so:
def mergeSort(a):
h = len(a)
mid = h / 2
if h >= 2:
return merge(mergeSort(a[:mid]), mergeSort(a[mid:]))
else:
return a
A few notes:
l is always 0, might as well remove it
h is len(a) - 1, but then you use h + 1, might as well use h = len(a)
Actually writting h >= 2 makes it clearer that you need at least 2 items in your list
mid can be len(a) / 2.
when taking the complete beginning/end of an array in a slice, the first/last bound is not required
I am trying to build an efficient function for splitting a list of any size by any given number of indices. This method works and it took me a few hours to get it right (I hate how easy it is to get things wrong when using indexes)
Am I over-thinking this?
Code:
def lindexsplit(List,*lindex):
index = list(lindex)
index.sort()
templist1 = []
templist2 = []
templist3 = []
breakcounter = 0
itemcounter = 0
finalcounter = 0
numberofbreaks = len(index)
totalitems = len(List)
lastindexval = index[(len(index)-1)]
finalcounttrigger = (totalitems-(lastindexval+1))
for item in List:
itemcounter += 1
indexofitem = itemcounter - 1
nextbreakindex = index[breakcounter]
#Less than the last cut
if breakcounter <= numberofbreaks:
if indexofitem < nextbreakindex:
templist1.append(item)
elif breakcounter < (numberofbreaks - 1):
templist1.append(item)
templist2.append(templist1)
templist1 = []
breakcounter +=1
else:
if indexofitem <= lastindexval and indexofitem <= totalitems:
templist1.append(item)
templist2.append(templist1)
templist1 = []
else:
if indexofitem >= lastindexval and indexofitem < totalitems + 1:
finalcounter += 1
templist3.append(item)
if finalcounter == finalcounttrigger:
templist2.append(templist3)
return templist2