I can easily add any Unitary gate which I create using Qiskit but it gives a warning when I try to add these rotation gates to the noise model basis gates. It might be a problem with the labelling(I used 'rx' and 'ry') or some other issues which I am not able to figure out.
The problem is the noise won't affect these gates unless I add them to the basis gates.
noise_model = NoiseModel()
error_1 = depolarizing_error(prob_1, 1)
error_2 = depolarizing_error(prob_2, 2)
noise_model.add_all_qubit_quantum_error(phase_damping_error(0.1), ['Z1','Z2','rx','ry','h'])
noise_model.add_all_qubit_quantum_error(phase_damping_error(0.1),['CZϕA','CZϕB'])
noise_model.add_basis_gates(['unitary','rx','ry'])
basis_gates = noise_model.basis_gates
circs = transpile([qc1, qc2, qc3], basis_gates=basis_gates)
result_error = execute(circs,
QasmSimulator(),
shots=10,
noise_model=noise_model,
basis_gates=basis_gates).result()
print(noise_model)
Warning: Adding a gate "rx" to basis_gates which is not in QasmSimulator basis_gates.
Warning: Adding a gate "ry" to basis_gates which is not in QasmSimulator basis_gates.
NoiseModel:
Basis gates: ['cx', 'h', 'id', 'u3', 'unitary']
Instructions with noise: ['CZϕB', 'Z2', 'CZϕA', 'rx', 'h', 'ry', 'Z1']
All-qubits errors: ['Z1', 'Z2', 'rx', 'ry', 'h', 'CZϕA', 'CZϕB']
Related
My program is optimizing the charging and decharging of a home battery to minimize the cost of electricity at the end of the year. In this case there also is a PV, which means that sometimes you're injecting electricity into the grid and receive money. The net offtake is the result of the usage of the home and the PV installation. So these are the possible situations:
Net offtake > 0 => usage home > PV => discharge from battery or take from grid
Net offtake < 0 => usage home < PV => charge battery or injection into grid
The electricity usage of homes is measured each 15 minutes, so I have 96 measurement point in 1 day. I want to optimilize the charging and decharging of the battery for 2 days, so that day 1 takes the usage of day 2 into account.
I wrote a controller that reads the data and gives each time the input values for 2 days to the optimization. With a rolling principle, it goes to the next 2 days and so on. Below you can see the code from my controller.
from gekko import GEKKO
from simulationModel2_2d_1 import getSimulation2
from exportModel2 import exportToExcelModel2
import numpy as np
#import matplotlib.pyplot as plt
import pandas as pd
import time
import math
# ------------------------ Import and read input data ------------------------
file = r'Data Sim 2.xlsx'
data = pd.read_excel(file, sheet_name='Input', na_values='NaN')
dataRead = pd.DataFrame(data, columns= ['Timestep','Verbruik woning (kWh)','Netto afname (kWh)','Prijs afname (€/kWh)',
'Prijs injectie (€/kWh)','Capaciteit batterij (kW)',
'Capaciteit batterij (kWh)','Rendement (%)',
'Verbruikersprofiel','Capaciteit PV (kWp)','Aantal dagen'])
timestep = dataRead['Timestep'].to_numpy()
usage_home = dataRead['Verbruik woning (kWh)'].to_numpy()
net_offtake = dataRead['Netto afname (kWh)'].to_numpy()
price_offtake = dataRead['Prijs afname (€/kWh)'].to_numpy()
price_injection = dataRead['Prijs injectie (€/kWh)'].to_numpy()
cap_batt_kW = dataRead['Capaciteit batterij (kW)'].iloc[0]
cap_batt_kWh = dataRead['Capaciteit batterij (kWh)'].iloc[0]
efficiency = dataRead['Rendement (%)'].iloc[0]
usersprofile = dataRead['Verbruikersprofiel'].iloc[0]
days = dataRead['Aantal dagen'].iloc[0]
pv = dataRead['Capaciteit PV (kWp)'].iloc[0]
# ------------- Optimization model & Rolling principle (2 days) --------------
# Initialise model
m = GEKKO()
# Output data
ts = []
charging = [] # Amount to charge/decharge batterij
e_batt = [] # Amount of energy in the battery
usage_net = [] # Usage after home, battery and pv
p_paid = [] # Price paid for energy of 15min
# Energy in battery to pass
energy = 0
# Iterate each day for one year
for d in range(int(days)-1):
d1_timestep = []
d1_net_offtake = []
d1_price_offtake = []
d1_price_injection = []
d2_timestep = []
d2_net_offtake = []
d2_price_offtake = []
d2_price_injection = []
# Iterate timesteps
for i in range(96):
d1_timestep.append(timestep[d*96+i])
d2_timestep.append(timestep[d*96+i+96])
d1_net_offtake.append(net_offtake[d*96+i])
d2_net_offtake.append(net_offtake[d*96+i+96])
d1_price_offtake.append(price_offtake[d*96+i])
d2_price_offtake.append(price_offtake[d*96+i+96])
d1_price_injection.append(price_injection[d*96+i])
d2_price_injection.append(price_injection[d*96+i+96])
# Input data simulation of 2 days
ts_temp = np.concatenate((d1_timestep, d2_timestep))
net_offtake_temp = np.concatenate((d1_net_offtake, d2_net_offtake))
price_offtake_temp = np.concatenate((d1_price_offtake, d2_price_offtake))
price_injection_temp = np.concatenate((d1_price_injection, d2_price_injection))
if(d == 7):
print(ts_temp)
print(energy)
# Simulatie uitvoeren
charging_temp, e_batt_temp, usage_net_temp, p_paid_temp, energy_temp = getSimulation2(ts_temp, net_offtake_temp, price_offtake_temp, price_injection_temp, cap_batt_kW, cap_batt_kWh, efficiency, energy, pv)
# Take over output first day, unless last 2 days
energy = energy_temp
if(d == (days-2)):
for t in range(1,len(ts_temp)):
ts.append(ts_temp[t])
charging.append(charging_temp[t])
e_batt.append(e_batt_temp[t])
usage_net.append(usage_net_temp[t])
p_paid.append(p_paid_temp[t])
elif(d == 0):
for t in range(int(len(ts_temp)/2)+1):
ts.append(ts_temp[t])
charging.append(charging_temp[t])
e_batt.append(e_batt_temp[t])
usage_net.append(usage_net_temp[t])
p_paid.append(p_paid_temp[t])
else:
for t in range(1,int(len(ts_temp)/2)+1):
ts.append(ts_temp[t])
charging.append(charging_temp[t])
e_batt.append(e_batt_temp[t])
usage_net.append(usage_net_temp[t])
p_paid.append(p_paid_temp[t])
print('Simulation day '+str(d+1)+' complete.')
# ------------------------ Export output data to Excel -----------------------
a = exportToExcelModel2(ts, usage_home, net_offtake, price_offtake, price_injection, charging, e_batt, usage_net, p_paid, cap_batt_kW, cap_batt_kWh, efficiency, usersprofile, pv)
print(a)
The optimization with Gekko happens in the following code:
from gekko import GEKKO
def getSimulation2(timestep, net_offtake, price_offtake, price_injection,
cap_batt_kW, cap_batt_kWh, efficiency, start_energy, pv):
# ---------------------------- Optimization model ----------------------------
# Initialise model
m = GEKKO(remote = False)
# Global options
m.options.SOLVER = 1
m.options.IMODE = 6
# Constants
speed_charging = cap_batt_kW/4
m.time = timestep
max_cap_batt = m.Const(value = cap_batt_kWh)
min_cap_batt = m.Const(value = 0)
max_charge = m.Const(value = speed_charging) # max battery can charge in 15min
max_decharge = m.Const(value = -speed_charging) # max battery can decharge in 15min
# Parameters
usage_home = m.Param(net_offtake)
price_offtake = m.Param(price_offtake)
price_injection = m.Param(price_injection)
# Variables
e_batt = m.Var(value=start_energy, lb = min_cap_batt, ub = max_cap_batt) # energy in battery
price_paid = m.Var() # price paid each 15min
charging = m.Var(lb = max_decharge, ub = max_charge) # amount charge/decharge each 15min
usage_net = m.Var(lb=min_cap_batt)
# Equations
m.Equation(e_batt==(m.integral(charging)+start_energy)*efficiency)
m.Equation(-charging <= e_batt)
m.Equation(usage_net==usage_home + charging)
price = m.Intermediate(m.if2(usage_net*1e6, price_injection, price_offtake))
price_paid = m.Intermediate(usage_net * price / 100)
# Objective
m.Minimize(price_paid)
# Solve problem
m.options.COLDSTART=2
m.solve()
m.options.TIME_SHIFT=0
m.options.COLDSTART=0
m.solve()
# Energy to pass
energy_left = e_batt[95]
#m.cleanup()
return charging, e_batt, usage_net, price_paid, energy_left
The data you need for input can be found in this Excel document:
https://docs.google.com/spreadsheets/d/1S40Ut9-eN_PrftPCNPoWl8WDDQtu54f0/edit?usp=sharing&ouid=104786612700360067470&rtpof=true&sd=true
With this code, it always ends at day 17 with the "Solution Not Found" Error.
I already tried extending the default iteration limit to 500 but it didn't work.
I also tried with other solvers but also no improvement.
By presolving with COLDSTART it already reached day 17, without this it ends at day 8.
I solved the days were my optimization ends individually and then the solution was always found immediately with the same code.
Someone who can explain this to me and maybe find a solution? Thanks in advance!
This is kind of big to troubleshoot, but here are some general ideas that might help. This assumes, as you said, that the model solves fine for day 1-2, and day 3-4, and day 5-6, etc. And that those results pass inspection (aka the basic model is working as you say).
Then something is (obviously) amiss around day 17. Some things to look at and try:
Start the model at day 16-17, see if it works in isolation
gather your results as you go and do a time series plot of the key variables, maybe one of them is on an obvious bad trend towards a limit causing an infeasibility... Perhaps the e_batt variable is slowly declining because not enough PV energy is available and hits minimum on Day 17
Radically change the upper/lower bounds on your variables to test them to see if they might be involved in the infeasibility (assuming there is one)
Make a different (fake) dataset where the solution is assured and kind of obvious... all constants or a pattern that you know will produce some known result and test the model outputs against it.
It might also be useful to pursue the tips in this excellent post on troubleshooting gekko, and edit your question with the results of that effort.
edit: couple ideas from your comment...
You didn't say what the result was from troubleshooting and whether the error is infeasible or max iterations, or ???. But...
If the model seems to crunch after about 15 days, I'm betting that it is becoming infeasible. Did you plot the battery level over course of days?
Also, I'm suspicious of your equation for the e_batt level... Why are you multiplying the prior battery state by the efficiency factor? That seems incorrect. That is charge that is already in the battery. Odds are you are (incorrectly) hitting the battery charge level every day with the efficiency tax and that the max charge level isn't sufficient to keep up with demand.
In addition to tips above try:
fix your efficiency formula to not multiply the efficiency times the previous state
change the efficiency to 100%
make the upper limit on charge huge
As an aside: I don't really see the connection to PV energy available here. What you are basically modeling is some "mystery battery" that you can charge and discharge anytime you want. I would get this debugged first and then look at the energy available by time of day...you aren't going to be generating charge at midnight. :).
I'm trying to identify a suitable bandwidth to use for a geographically weighted regression but every time I search for the bandwidth it displays that there are missing (NaN) values within the arrays of the dataset. Although, each row features all values.
g_y = df_ct2008xy['2008 HP'].values.reshape((-1,1))
g_X = df_ct2008xy[['2008 AF', '2008 MI', '2008 MP', '2008 EB']].values
u = df_ct2008xy['X']
v = df_ct2008xy['Y']
g_coords = list(zip(u,v))
g_X = (g_X - g_X.mean(axis=0)) / g_X.std(axis=0)
g_y = g_y.reshape((-1,1))
g_y = (g_y - g_y.mean(axis=0)) / g_y.std(axis=0)
bw = mgwr.sel_bw.Sel_BW(g_coords,
g_y, # Independent variable
g_X, # Dependent variable
fixed=True, # True for fixed bandwidth and false for adaptive bandwidth
spherical=True) # Spherical coordinates (long-lat) or projected coordinates
I searched using numpy to identify if these were individual values using
np.isnan(g_y).any()
and
np.isnan(g_X)
but apparently every value is 'missing' and returning 'True'
I'm implementing a DQN to do the Trading in the stock market (for educational purposes only)
I have this data and the shape of the data. This is the state in a time series data and I'm going to pass it to a nerual network. The first column is the Closing price of a stock, and the second column is the Volume (Normalized already):
array([[[-0.39283217, 3.96508668],
[-0.39415516, 0.04931261],
[-0.38271683, -0.34029827],
[-0.39283217, -0.42384451],
[-0.4332384 , -0.11795849],
[-0.41201548, -0.47441503],
[-0.41739012, -0.51788375],
[-0.42210326, -0.60101319],
[-0.43660099, -0.596672 ],
[-0.43660099, -0.64244935]]])
(1, 10, 2)
Now I pass this data to a neural network. It's essentially a policy network, but to simplify the question I write it like this here (The loss is Q - target Q value):
model = keras.Sequential([
keras.layers.Input(shape=(10,2,)),
keras.layers.Dense(10, activation='relu'),
keras.layers.Dense(3, activation='linear')
])
model.compile(loss=count_the_loss(),
optimizer='adam',
metrics='mse')
Now I get this by using the predict function:
array([[[-0.79352564, -0.22876596, 2.309589 ],
[-0.10996505, 0.01430818, 0.22286436],
[-0.17374574, 0.03645202, 0.10073717],
[-0.19824156, 0.07159233, 0.08594725],
[-0.12234195, 0.03734204, 0.19439939],
[-0.21589771, 0.088783 , 0.08315123],
[-0.22866695, 0.10703149, 0.07550874],
[-0.25188142, 0.1436682 , 0.05827002],
[-0.25386256, 0.13714936, 0.06612003],
[-0.26608405, 0.1581351 , 0.05540368]]], dtype=float32)
I'm supposed to get the q(s,a1), q(s,a2), q(s,a3) (where a1, a2 and a3 stands for actions: short, flat and long respectively), then find the q for the action sampled from the experience replay.
But now I get a 1x10x3 array.
My questions are:
How am I supposes to get the q?
And when this is done, it's time to find the target Q. It's similar to the process above. Suppose the above result is what I get by passing the next_state to a target network. I have to find the q max. How can I find q max in a 1x10x3 array?
How can I easily compare the distributions of multiple cohorts?
Usually, https://seaborn.pydata.org/generated/seaborn.distplot.html would be a great tool to visually compare distributions. However, due to the size of my dataset, I needed to compress it and only keep the counts.
It was created as:
SELECT age, gender, compress_distributionUDF(collect_list(struct(target_y_n, count, distribution_value))) GROUP BY age, gender
where compress_distributionUDF simply takes a list of tuples and returns the counts per group.
This leaves me with a list of
Row(distribution_value=60.0, count=314251, target_y_n=0)
nested inside a pandas.Series, but one per each chohort.
Basically, it is similar to:
pd.DataFrame({'foo':[1,2], 'bar':['first', 'second'], 'baz':[{'target_y_n': 0, 'value': 0.5, 'count':1000},{'target_y_n': 1, 'value': 1, 'count':10000}]})
and I wonder how to compare distributions:
within a cohort 0 vs. 1 of target_y_n
over multiple cohorts
in a way which is visually still understandable and not only a mess.
edit
For a single cohort Plotting pre aggregated data in python could be the answer, but how can multiple cohorts be compared (not just in a loop) as this leads to too many plots to compare?
I am still quite confused but we can start from this and see where it goes. From your example, I am focusing on baz as it is not clear to me what foo and bar are (I assume cohorts).
So let focus on baz and plot the different distributions according to target_y_n.
sns.catplot('value','count',data=df, kind='bar',hue='target_y_n',dodge=False,ci=None)
sns.catplot('value','count',data=df, kind='box',hue='target_y_n',dodge=False)
plt.bar(df[df['target_y_n']==0]['value'],df[df['target_y_n']==0]['count'],width=1)
plt.bar(df[df['target_y_n']==1]['value'],df[df['target_y_n']==1]['count'],width=1)
plt.legend(['Target=0','Target=1'])
sns.barplot('value','count',data=df, hue = 'target_y_n',dodge=False,ci=None)
Finally try to have a look at the FacetGrid class to extend your comparison (see here).
g=sns.FacetGrid(df,col='target_y_n',hue = 'target_y_n')
g=g.map(sns.barplot,'value','count',ci=None)
In your case you would have something like:
g=sns.FacetGrid(df,col='target_y_n',row='cohort',hue = 'target_y_n')
g=g.map(sns.barplot,'value','count',ci=None)
And a qqplot option:
from scipy import stats
def qqplot(x, y, **kwargs):
_, xr = stats.probplot(x, fit=False)
_, yr = stats.probplot(y, fit=False)
plt.scatter(xr, yr, **kwargs)
g=sns.FacetGrid(df,col='cohort',hue = 'target_y_n')
g=g.map(qqplot,'value','count')
I am trying to apply k-means clustering on a set of images (images are loaded as float torch.Tensors) using the following segment of code:
print('[Clustering all samples...]')
local points = torch.Tensor(trsize, 3, 221, 221)
for i = 1,trsize do
points[i] = trainData.data[i]:clone() -- dont want to modify the original tensors
end
points:resize(trsize, 3*221*221) -- to convert it to a 2-D tensor
local centroids, counts = unsup.kmeans(points, total_classes, 40, total_classes, nil, true)
print(counts)
When I observe the values in the counts tensor, I observe that it contains unexpected values, in the form of some entries being more than trsize, whereas the documentation says that counts stores the counts per centroid. I expected that it means counts[i] equals the number of samples out of trsize belonging to cluster with centroid centroids[i]. Am I wrong in assuming so?
If that indeed is the case, shouldn't sample-to-centroid be a hard-assignment (i.e. shouldn't counts[i] sum to trsize, which clearly is not the case with my clustering)? Am I missing something here?
Thanks in advance.
In the current version of the code, counts are accumulated after each iteration
for i = 1,niter do
-- k-means computations...
-- total counts
totalcounts:add(counts)
end
So in the end counts:sum() is a multiple of niter.
As a workaround you can use the callback to obtain the final counts (non-accumulated):
local maxiter = 40
local centroids, counts = unsup.kmeans(
points,
total_classes,
maxiter,
total_classes,
function(i, _, totalcounts) if i < maxiter then totalcounts:zero() end end,
true
)
As an alternative you can use vlfeat.torch and explicitly quantize your input points after kmeans to obtain these counts:
local assignments = kmeans:quantize(points)
local counts = torch.zeros(total_classes):int()
for i=1,total_classes do
counts[i] = assignments:eq(i):sum()
end