I have a graph composed of 742 edges, and 360 nodes.
I want to compute max flow between some pairs of nodes and it happens, for some of them the nx.maximum_flow ends with the pasted error, despite the fact that a path exists between the two concerned nodes.
Any idea what causes that?
Thanks.
ValueError Traceback (most recent call last)
<ipython-input-186-6dae3501e3d0> in <module>()
1 #print(nx.shortest_path(G,source="Sink_0",target="node_32"))
----> 2 nx.maximum_flow(G, "Sink_0", "Aircraft2_32")
/Library/Python/2.7/site-packages/networkx/algorithms/flow/maxflow.pyc in maximum_flow(G, s, t, capacity, flow_func, **kwargs)
156 raise nx.NetworkXError("flow_func has to be callable.")
157
--> 158 R = flow_func(G, s, t, capacity=capacity, value_only=False, **kwargs)
159 flow_dict = build_flow_dict(G, R)
160
/Library/Python/2.7/site-packages/networkx/algorithms/flow/preflowpush.pyc in preflow_push(G, s, t, capacity, residual, global_relabel_freq, value_only)
420 """
421 R = preflow_push_impl(G, s, t, capacity, residual, global_relabel_freq,
--> 422 value_only)
423 R.graph['algorithm'] = 'preflow_push'
424 return R
/Library/Python/2.7/site-packages/networkx/algorithms/flow/preflowpush.pyc in preflow_push_impl(G, s, t, capacity, residual, global_relabel_freq, value_only)
279 break
280 u = next(iter(level.active))
--> 281 height = discharge(u, False)
282 if grt.is_reached():
283 # Global relabeling heuristic.
/Library/Python/2.7/site-packages/networkx/algorithms/flow/preflowpush.pyc in discharge(u, is_phase1)
156 # We have run off the end of the adjacency list, and there can
157 # be no more admissible edges. Relabel the node to create one.
--> 158 height = relabel(u)
159 if is_phase1 and height >= n - 1:
160 # Although the node is still active, with a height at least
/Library/Python/2.7/site-packages/networkx/algorithms/flow/preflowpush.pyc in relabel(u)
125 """
126 grt.add_work(len(R_succ[u]))
--> 127 return min(R_node[v]['height'] for v, attr in R_succ[u].items()
128 if attr['flow'] < attr['capacity']) + 1
129
ValueError: min() arg is an empty sequence
Related
Looking to take a list of stocks and adjust their weight in a portfolio until the overall portfolio beta is 1.0 the output of "stonkBetas" is static and is:
[3.19292010501853,
0.7472001935364129,
1.0889157697158605,
0.8944059912707691,
0.04192080860817828,
1.0011520737327186,
0.9155119223385676]
I then create two functions. One to define how the betas are weighted. The second just as con that will make the constraint that the sum of the minimized weighted portfolio will have an overall summed beta of 1.0.
def betaOpp(weights):
a,b,c,d,e,f,g=weights
f=a*stonkBetas[0]+b*stonkBetas[1]+c*stonkBetas[2]+d*stonkBetas[3]+e*stonkBetas[4]+f*stonkBetas[5]+g*stonkBetas[6]
return f
initial_guess = [.1,.1,.1,.1,.2,.2,.2]
print('hi')
print(sum(initial_guess))
print('bye')
def con(t):
print('this should be zero:')
print(sum(t)-1)
return sum(t) - 1.0
cons = {'type':'eq', 'fun': con}
bnds = ((.02,.8),(.02,.8),(.02,.8),(.02,.8),(.02,.8),(.02,.8),(.02,.8))
res = optimize.minimize(betaOpp,initial_guess, bounds=bnds, constraints=cons)
print(res)
This gives me this output
hi
1.0
bye
this should be zero:
0.0
this should be zero:
0.0
this should be zero:
0.0
this should be zero:
1.4901161193847656e-08
this should be zero:
1.4901161193847656e-08
this should be zero:
1.4901161193847656e-08
this should be zero:
1.4901161193847656e-08
this should be zero:
1.4901161193847656e-08
this should be zero:
1.4901161193847656e-08
this should be zero:
1.4901161193847656e-08
this should be zero:
6.661338147750939e-16
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-30-6567109e94a4> in <module>
16 cons = {'type':'eq', 'fun': con}
17 bnds = ((.02,.8),(.02,.8),(.02,.8),(.02,.8),(.02,.8),(.02,.8),(.02,.8))
---> 18 res = optimize.minimize(betaOpp,x0=initial_guess, bounds=bnds, constraints=cons)
19 print(res)
/opt/miniconda3/lib/python3.6/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
624 elif meth == 'slsqp':
625 return _minimize_slsqp(fun, x0, args, jac, bounds,
--> 626 constraints, callback=callback, **options)
627 elif meth == 'trust-constr':
628 return _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp,
/opt/miniconda3/lib/python3.6/site-packages/scipy/optimize/slsqp.py in _minimize_slsqp(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, finite_diff_rel_step, **unknown_options)
424
425 if mode == -1: # gradient evaluation required
--> 426 g = append(sf.grad(x), 0.0)
427 a = _eval_con_normals(x, cons, la, n, m, meq, mieq)
428
/opt/miniconda3/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in grad(self, x)
186 if not np.array_equal(x, self.x):
187 self._update_x_impl(x)
--> 188 self._update_grad()
189 return self.g
190
/opt/miniconda3/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in _update_grad(self)
169 def _update_grad(self):
170 if not self.g_updated:
--> 171 self._update_grad_impl()
172 self.g_updated = True
173
/opt/miniconda3/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in update_grad()
90 self.ngev += 1
91 self.g = approx_derivative(fun_wrapped, self.x, f0=self.f,
---> 92 **finite_diff_options)
93
94 self._update_grad_impl = update_grad
/opt/miniconda3/lib/python3.6/site-packages/scipy/optimize/_numdiff.py in approx_derivative(fun, x0, method, rel_step, abs_step, f0, bounds, sparsity, as_linear_operator, args, kwargs)
389
390 if np.any((x0 < lb) | (x0 > ub)):
--> 391 raise ValueError("`x0` violates bound constraints.")
392
393 if as_linear_operator:
ValueError: `x0` violates bound constraints.
And I just don't understand where I'm going wrong. The x0 is perfectly 1.0 - I can see it! Hopefully I'm just doing something stupid here. Please help!
I try to decompose a sparse matrix(40,000×1,400,000) with scipy.sparse.linalg.svds on my 64-bit machine with 140GB RAM. as following:
k = 5000
tfidf_mtx = tfidf_m.tocsr()
u_45,s_45,vT_45 = scipy.sparse.linalg.svds(tfidf_mtx, k=k)
When the K ranges from 1000 to 4500, it works. But the K is 5000, it throws an MemoryError.The precise error is given below:
---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
<ipython-input-6-31a69ce54e2c> in <module>()
4 k = 4000
5 tfidf_mtx = tfidf_m.tocsr()
----> 6 get_ipython().magic(u'time u_50,s_50,vT_50 =linalg.svds(tfidf_mtx, k=k))
7 # print len(s),s
8
/usr/lib/python2.7/dist-packages/IPython/core/interactiveshell.pyc in magic(self, arg_s)
2163 magic_name, _, magic_arg_s = arg_s.partition(' ')
2164 magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
-> 2165 return self.run_line_magic(magic_name, magic_arg_s)
2166
2167 #-------------------------------------------------------------------------
/usr/lib/python2.7/dist-packages/IPython/core/interactiveshell.pyc in run_line_magic(self, magic_name, line)
2084 kwargs['local_ns'] = sys._getframe(stack_depth).f_locals
2085 with self.builtin_trap:
-> 2086 result = fn(*args,**kwargs)
2087 return result
2088
/usr/lib/python2.7/dist-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)
/usr/lib/python2.7/dist-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k)
189 # but it's overkill for just that one bit of state.
190 def magic_deco(arg):
--> 191 call = lambda f, *a, **k: f(*a, **k)
192
193 if callable(arg):
/usr/lib/python2.7/dist-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)
1043 else:
1044 st = clock2()
-> 1045 exec code in glob, local_ns
1046 end = clock2()
1047 out = None
<timed exec> in <module>()
/usr/local/lib/python2.7/dist-packages/scipy/sparse/linalg/eigen/arpack/arpack.pyc in svds(A, k, ncv, tol, which, v0, maxiter, return_singular_vectors)
1751 else:
1752 ularge = eigvec[:, above_cutoff]
-> 1753 vhlarge = _herm(X_matmat(ularge) / slarge)
1754
1755 u = _augmented_orthonormal_cols(ularge, nsmall)
/usr/local/lib/python2.7/dist-packages/scipy/sparse/base.pyc in dot(self, other)
244
245 """
--> 246 return self * other
247
248 def __eq__(self, other):
/usr/local/lib/python2.7/dist-packages/scipy/sparse/base.pyc in __mul__(self, other)
298 return self._mul_vector(other.ravel()).reshape(M, 1)
299 elif other.ndim == 2 and other.shape[0] == N:
--> 300 return self._mul_multivector(other)
301
302 if isscalarlike(other):
/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.pyc in _mul_multivector(self, other)
463
464 result = np.zeros((M,n_vecs), dtype=upcast_char(self.dtype.char,
--> 465 other.dtype.char))
466
467 # csr_matvecs or csc_matvecs
MemoryError:
The when the k is 3000 and 4500, the ratio of the sum of the square of singular values to the sum of the square of all matrix entities is respectively 0.7033 and 0.8230. I am searching for a long time on net. But no use. Please help or try to give some ideas how to achieve this.
So the return is an (M,k) array. On an ordinary older machine:
In [368]: np.ones((40000,1000))
....
In [369]: np.ones((40000,4000))
...
In [370]: np.ones((40000,5000))
...
--> 190 a = empty(shape, dtype, order)
191 multiarray.copyto(a, 1, casting='unsafe')
192 return a
MemoryError:
Now may just be a coincidence that I hit the memory error at the same size are your code. But if you make the problem big enough you will hit memory errors at some point.
Your stacktrace shows the error occurs while multiplying a sparse matrix and a dense 2d array (other), and the result will be dense as well.
I need to write a code to display the location of the highest and lowest tx value. Nothing appears to be working. Here is my code:
%times
tx=[tf-to];
tx=[130 103 152 163 218 278 82 195 221 154 94 159 214 185];
s=(130+103+52+163+218+278+82+195+221+154+94+159+214+185);
%minimum and maximum times
minvalue=min(tx);
maxvalue=max(tx);
How do I edit this code to show the max and min values of tx only??
[minvalue,idx_min]=min(tx);
[maxvalue,idx_max]=max(tx);
This uses the second output of both min and max, which returns the index of the min/max value respectively.
Adding two inline functions to return the min and max is a possibility.
min_index = #(vector) find(vector==min(vector))
max_index = #(vector) find(vector==max(vector))
idx_min = min_index(tx);
idx_max = max_index(tx);
I am looking to classify the values of a matrix. The following example works outside of a parfor loop, however it does not work when used within a parfor loop. What are my options to classify matrices, following the provided example, within a parfor loop?
% Sample data
Imag1 = [ 62 41 169 118 210;
133 158 96 149 110;
211 200 84 194 29;
209 16 15 146 28;
95 144 13 249 170];
% Perform the classification
Imag1(find(Imag1 <= 130)) = 4;
Imag1(find(Imag1 >= 150)) = 1;
Imag1(find(Imag1 > 140)) = 2;
Imag1(find(Imag1 > 130)) = 3;
Results in the following (outside parfor loop):
Imag1 =
62 41 169 118 210
133 158 96 149 110
211 200 84 194 29
209 16 15 146 28
95 144 13 249 170
Imag1 =
4 4 1 4 1
3 1 4 2 4
1 1 4 1 4
1 4 4 2 4
4 2 4 1 1
You can use another matrix to store the result.
Imag2 = zeros(size(Imag1));
% Perform the classification
Imag2(find(Imag1 <= 130)) = 4;
Imag2(find(Imag1 >= 150)) = 1;
Imag2(find(Imag1 > 140)) = 2;
Imag2(find(Imag1 > 130)) = 3;
so you can use a parfor loop somehow. Since parfor doesn't care about order of execution. Your code doesn't work in a parfor loop because right now you are modifying the values of Imag1, then comparing those values again, aka any loop iteration would be be dependent on the prior loop iterations
Here's another approach:
parfor c = 1:size(Imag1,2)
Imag2(:,c) = 4-min(ceil(max((Imag1(:,c) - 130),0)/10),3);
end
Now you can split it up however you like; by rows, by columns, by blocks, by alternate columns...
Edit:
Unfortunately this classifies a value of 150 as 2. So you probably shouldn't use this one.
Take 3
class = [150 141 131 0]; %// minimum values for class 1, 2, 3, 4
[m, n] = size(Imag1);
parfor c=1:n
for r=1:m
Imag3(r,c) = find(Imag1(r,c) >= class, 1);
end
end
I have to make a matlab program, which should create a QR Code.
My problem is the Reed Solomon error correction
The user enters the word he wants. [...] I got a string of numbers I should be gone in a polynomial generator (Reed Solomon) (I found some sites that do this very well: http://www.pclviewer.com/rs2/calculator.html)
I would like it to happen: for example I input: 32 91 11 120 209 114 220 77 67 64 236 17 236
[Reed Solomon generator polynomial]
and I want to find out: 168 72 22 82 217 54 156 0 46 15 180 122 16
I found the functions rsenc comm.rsencoder gf ... But it is impossible to understand the operation of these functions. Functions are detailed: http://www.mathworks.fr/fr/help/comm...n.html#fp12225
I tried a code of this type :
n = 255; k = 13; % Codeword length and message length
m = 8; % Number of bits in each symbol
msg = [32 91 11 120 209 114 220 77 67 64 236 17 236]; % Message is a Galois array.
obj = comm.RSEncoder(n, k);
c1 = step(obj, msg(1,:)');
c = [c1].';
He produced a string of 255 while I want 13 output.
Thank you for your help.
I think that you are committing a mistake.
'n' is the length of final message with parity code.
'k' is the lenght of message (number of symbols)
I guess that this will help you:
clc, clear all;
M = 16; % Modulation Order || same that Max value, at your case: 256! 2^m
hEnc = comm.RSEncoder;
hEnc.CodewordLength = M - 1; % Max = M-1, Min = 4, Must be greater than MessageLenght
hEnc.MessageLength = 13; % Experiment change up and down value (using odd number)
hEnc.BitInput = false;
hEnc
t = hEnc.CodewordLength - hEnc.MessageLength;
frame = 2*hEnc.MessageLength; % multiple of MensagemLength
fprintf('\tError Detection (in Symbols): %d\n',t);
fprintf('\tError Correction: %.2f\n',t/2);
data = randi([0 M-1], frame, 1); % Create a frame with symbols range (0 to M-1)
encodedData = step(hEnc, data); % encod the frame