jamesrobertlloyd / gp-structure-search Goto Github PK

Make it read as well as abstract and Jaynes quotation

This might fix some bugs of extraneous kernels being discovered - the algorithm might be working round a stuck optimiser.

This would let us make a lot more interesting claims in the paper - we could claim to address nonstationarity. It would also make the decomposition algorithm more interesting than simply distributing terms.

Write code to plot separately the posterior mean and marginal variance of each component of the signal.

Produces weird noise in the pdfs - any guesses why?

Too much data!

Is it well-calibrated across different scales?

Kernel over kernels + active learning / Bayesian optimisation
Priors on expressions
...

Remove dependence on config
Add GPML
Make parallelism less location dependent (picloud?)
...

This kernel

ScoredKernel(k_opt=ProductKernel([ MaskKernel(ndim=8, active_dimension=0, base_kernel=CubicKernel(offset=1.757755, output_variance=7.084045)), MaskKernel(ndim=8, active_dimension=7, base_kernel=SqExpPeriodicKernel(lengthscale=-2.701080, period=-0.380918, output_variance=-0.071214)) ]), nll=6348.096611, laplace_nle=-184450132.068237, bic_nle=12720.630212, noise=[-1.77276072])

applied to data set r_concrete_500_fold_10_of_10.mat

has a clearly erroneous laplace_nle

Why?

I prefer the kernel conceptually - but need to check if numerically stable

Might be useful when removing kernels - not sure - seemed sensible a while back

Why do they get sprinkled into expressions - seemingly changing very little?

This means both

• ensuring local extrema are reached so Laplace approx valid
• jittering previously learned parameter values to avoid having to multiply by constant kernels

Move unused results files so that they can be nuked if necessary

Hessian delta of 1e-6 will not always be appropriate e.g. location parameters for unscaled data

Make them shift and scale with the data appropriately

Which licence? GPL, CRAPL, MIT?

Regression is a super-developed field, but the related work section is currently a bit too long.

Either with priors + MAP (i.e. regularisation) or a more Bayesian treatment (Laplace, sampling...?)

Hi,

I am trying to run this code base on windows 10 and I am running into a number of issues after fixing the file paths and similar benign issues I ran into the following MATLAB error when running demo.py:

For online documentation, see http://www.mathworks.com/support
For product information, visit www.mathworks.com.

Undefined function or variable 'tmpb0_jih'.

Could you please assist?

Thank you

See readme for details

• sh -> cmd
• string sanitization

And Josh's examples. We might consider making nice 2D plots for the EEG data, as well.

Zipping/unzipping occasionally results in perhaps unreadable files - they have a job id of 'access' - clearly something is wrong

In general, there is a risk of constant looping when a job is submitted but it doesn't show up on qstat yet - need to detect this looping behaviour

Merge LOCAL and HOME - and put fully within cblparallel

Prevent periods smaller than smallest data distance to prevent trivial aliasing

In general, what are 'sensible' default values

or just shrink them into one table

If any code fails (e.g. ill conditioned covariance matrix) the experiment can get stuck in an infinite loop

I observed this in the prediction code once - quick fix was to change the starting noise parameter very slightly.

Dear authors,

I am thankful that you share the code in the Github.

I am really like your paper and try to run your code on your demo.py.

However, when I go to the source directory and run demo.py. This is an error on my computer:
"reset: unknown terminal type unknown
Terminal type? "
Could you please give me some suggestion about that?

The below is all the information when I run the demo.py.

"
runfile('/home/chunchao/Desktop/gp-structure-search-master/source/demo.py', wdir='/home/chunchao/Desktop/gp-structure-search-master/source')
Backend TkAgg is interactive backend. Turning interactive mode on.
Running experiment:
description = An example experiment,
data_dir = ../data/1d_data/01-airline.mat,
max_depth = 2,
random_order = False,
k = 1,
debug = False,
local_computation = True,
n_rand = 2,
sd = 4,
max_jobs = 500,
verbose = True,
make_predictions = False,
skip_complete = False,
results_dir = ../examples/,
iters = 10,
base_kernels = SE,Per,Lin,
zero_mean = True,
verbose_results = False,
random_seed = 0,
use_min_period = True,
period_heuristic = 10,
use_constraints = False,
alpha_heuristic = -2,
lengthscale_heuristic = -4.5,
Experiment 01-airline
Output to: ../examples/01-airline_result.txt
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All new results:
1251.6857440777076 nan 2513.311114754567 M(0, SE(ell=-0.8, sf=5.7))
1181.7783896324722 nan 2373.4964058640962 M(0, LN(off=-2.0, ell=0.0, loc=0.0))
1180.6405321123773 nan 2371.2206908239064 M(0, SE(ell=3.0, sf=6.2))
1027.923777964088 nan 2065.787182527328 M(0, LN(off=-7.3, ell=7.0, loc=1944.7))
1027.903814739796 nan 2065.7472560787437 M(0, SE(ell=0.0, sf=0.0))
1027.8720632958796 nan 2065.683753190911 M(0, LN(off=-8.7, ell=0.1, loc=1955.4))
1023.5392001361355 nan 2061.987840170999 M(0, PE(ell=1.5, p=2.2, sf=5.2))
1023.3361754305673 nan 2061.5817907598625 M(0, PE(ell=3.4, p=1.8, sf=4.9))
All new results after duplicate removal:
1251.6857440777076 nan 2513.311114754567 M(0, SE(ell=-0.8, sf=5.7))
1181.7783896324722 nan 2373.4964058640962 M(0, LN(off=-2.0, ell=0.0, loc=0.0))
1180.6405321123773 nan 2371.2206908239064 M(0, SE(ell=3.0, sf=6.2))
1027.923777964088 nan 2065.787182527328 M(0, LN(off=-7.3, ell=7.0, loc=1944.7))
1027.903814739796 nan 2065.7472560787437 M(0, SE(ell=0.0, sf=0.0))
1027.8720632958796 nan 2065.683753190911 M(0, LN(off=-8.7, ell=0.1, loc=1955.4))
1023.5392001361355 nan 2061.987840170999 M(0, PE(ell=1.5, p=2.2, sf=5.2))
1023.3361754305673 nan 2061.5817907598625 M(0, PE(ell=3.4, p=1.8, sf=4.9))
Printing all results
1251.6857440777076 nan 2513.311114754567 M(0, SE(ell=-0.8, sf=5.7))
1181.7783896324722 nan 2373.4964058640962 M(0, LN(off=-2.0, ell=0.0, loc=0.0))
1180.6405321123773 nan 2371.2206908239064 M(0, SE(ell=3.0, sf=6.2))
1027.923777964088 nan 2065.787182527328 M(0, LN(off=-7.3, ell=7.0, loc=1944.7))
1027.903814739796 nan 2065.7472560787437 M(0, SE(ell=0.0, sf=0.0))
1027.8720632958796 nan 2065.683753190911 M(0, LN(off=-8.7, ell=0.1, loc=1955.4))
1023.5392001361355 nan 2061.987840170999 M(0, PE(ell=1.5, p=2.2, sf=5.2))
1023.3361754305673 nan 2061.5817907598625 M(0, PE(ell=3.4, p=1.8, sf=4.9))
Seed kernels :
M(0, PE(ell=3.4, p=1.8, sf=4.9))
Expanded kernels :
( M(0, LN(off=-2.0, ell=0.0, loc=0.0)) x M(0, PE(ell=3.4, p=1.8, sf=4.9)) )
( M(0, PE(ell=0.0, p=-2.0, sf=0.0)) x M(0, PE(ell=3.4, p=1.8, sf=4.9)) )
( M(0, PE(ell=3.4, p=1.8, sf=4.9)) x M(0, SE(ell=0.0, sf=0.0)) )
( M(0, LN(off=-2.0, ell=0.0, loc=0.0)) + M(0, PE(ell=3.4, p=1.8, sf=4.9)) )
( M(0, PE(ell=0.0, p=-2.0, sf=0.0)) + M(0, PE(ell=3.4, p=1.8, sf=4.9)) )
( M(0, PE(ell=3.4, p=1.8, sf=4.9)) + M(0, SE(ell=0.0, sf=0.0)) )
M(0, LN(off=-2.0, ell=0.0, loc=0.0))
M(0, PE(ell=0.0, p=-2.0, sf=0.0))
M(0, SE(ell=0.0, sf=0.0))
Creating data file locally
Creating scripts
Sending scripts to cblparallel
Submitting job 1 of 27
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All new results:
1231.2193188407873 nan 2482.3178908798786 ( M(0, PE(ell=3.4, p=1.8, sf=4.6)) x M(0, SE(ell=-0.0, sf=3.7)) )
1195.301552405626 nan 2410.482358009556 ( M(0, LN(off=-2.0, ell=0.1, loc=0.0)) x M(0, PE(ell=3.4, p=1.8, sf=4.9)) )
1180.5636329338015 nan 2390.946145665059 ( M(0, PE(ell=4.6, p=6.9, sf=-2.7)) + M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1180.5492496798781 nan 2390.917379157212 ( M(0, PE(ell=1.9, p=6.9, sf=3.3)) + M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1181.7816979119368 nan 2388.4124623217535 ( M(0, LN(off=-2.0, ell=0.0, loc=0.0)) + M(0, PE(ell=3.4, p=1.8, sf=4.9)) )
1180.563652648244 nan 2385.976371794368 ( M(0, PE(ell=3.4, p=1.8, sf=5.0)) + M(0, SE(ell=0.0, sf=-0.0)) )
1180.5636337552983 nan 2385.9763340084764 ( M(0, LN(off=-11.8, ell=3.4, loc=1960.9)) + M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1180.5636331360015 nan 2385.976332769883 ( M(0, PE(ell=3.4, p=1.8, sf=5.0)) + M(0, SE(ell=0.7, sf=-2.1)) )
1180.5636329755807 nan 2385.9763324490414 ( M(0, LN(off=-13.6, ell=5.1, loc=1970.5)) + M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1181.7125696761482 nan 2383.3043925506004 ( M(0, PE(ell=3.4, p=1.8, sf=4.9)) x M(0, SE(ell=0.0, sf=-0.0)) )
1180.663863187727 nan 2376.237166274182 M(0, PE(ell=2.9, p=1.0, sf=6.3))
1181.7783896324722 nan 2373.4964058640962 M(0, LN(off=-2.0, ell=0.0, loc=0.0))
1181.4844478710331 nan 2372.908522341218 M(0, SE(ell=4.4, sf=3.8))
1027.6078027525305 nan 2080.064672002941 ( M(0, PE(ell=3.6, p=6.3, sf=-4.3)) x M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1027.9237881880226 nan 2070.7570162747734 M(0, PE(ell=0.0, p=1.9, sf=-5.8))
1023.6929509234078 nan 2067.2651550451196 ( M(0, PE(ell=3.4, p=1.8, sf=6.1)) x M(0, SE(ell=-0.3, sf=-0.6)) )
1027.911635944512 nan 2065.762898488176 M(0, SE(ell=3.8, sf=-1.0))
1027.903814739796 nan 2065.7472560787437 M(0, SE(ell=0.0, sf=0.0))
1027.714691100425 nan 2065.3690088000017 M(0, LN(off=-8.4, ell=0.8, loc=1959.7))
1002.522711842176 nan 2024.924676882656 ( M(0, LN(off=-3.4, ell=1.0, loc=1965.1)) x M(0, PE(ell=3.3, p=0.7, sf=5.1)) )
974.4348088970949 nan 1958.8092443933417 M(0, LN(off=-15.0, ell=-1.1, loc=1967.5))
897.9109529202489 nan 1815.7011590388017 ( M(0, LN(off=-0.8, ell=-2.2, loc=1975.3)) x M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
894.5560567068537 nan 1813.9611799115874 ( M(0, PE(ell=3.4, p=1.8, sf=5.0)) + M(0, SE(ell=-0.8, sf=2.0)) )
827.4239624014712 nan 1679.6969913008222 ( M(0, PE(ell=4.6, p=1.6, sf=7.2)) x M(0, PE(ell=3.5, p=2.2, sf=4.8)) )
All new results after duplicate removal:
1231.2193188407873 nan 2482.3178908798786 ( M(0, PE(ell=3.4, p=1.8, sf=4.6)) x M(0, SE(ell=-0.0, sf=3.7)) )
1195.301552405626 nan 2410.482358009556 ( M(0, LN(off=-2.0, ell=0.1, loc=0.0)) x M(0, PE(ell=3.4, p=1.8, sf=4.9)) )
1180.5636329338015 nan 2390.946145665059 ( M(0, PE(ell=4.6, p=6.9, sf=-2.7)) + M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1180.5492496798781 nan 2390.917379157212 ( M(0, PE(ell=1.9, p=6.9, sf=3.3)) + M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1181.7816979119368 nan 2388.4124623217535 ( M(0, LN(off=-2.0, ell=0.0, loc=0.0)) + M(0, PE(ell=3.4, p=1.8, sf=4.9)) )
1180.563652648244 nan 2385.976371794368 ( M(0, PE(ell=3.4, p=1.8, sf=5.0)) + M(0, SE(ell=0.0, sf=-0.0)) )
1180.5636337552983 nan 2385.9763340084764 ( M(0, LN(off=-11.8, ell=3.4, loc=1960.9)) + M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1180.5636331360015 nan 2385.976332769883 ( M(0, PE(ell=3.4, p=1.8, sf=5.0)) + M(0, SE(ell=0.7, sf=-2.1)) )
1180.5636329755807 nan 2385.9763324490414 ( M(0, LN(off=-13.6, ell=5.1, loc=1970.5)) + M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1181.7125696761482 nan 2383.3043925506004 ( M(0, PE(ell=3.4, p=1.8, sf=4.9)) x M(0, SE(ell=0.0, sf=-0.0)) )
1180.663863187727 nan 2376.237166274182 M(0, PE(ell=2.9, p=1.0, sf=6.3))
1181.7783896324722 nan 2373.4964058640962 M(0, LN(off=-2.0, ell=0.0, loc=0.0))
1181.4844478710331 nan 2372.908522341218 M(0, SE(ell=4.4, sf=3.8))
1027.6078027525305 nan 2080.064672002941 ( M(0, PE(ell=3.6, p=6.3, sf=-4.3)) x M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1027.9237881880226 nan 2070.7570162747734 M(0, PE(ell=0.0, p=1.9, sf=-5.8))
1023.6929509234078 nan 2067.2651550451196 ( M(0, PE(ell=3.4, p=1.8, sf=6.1)) x M(0, SE(ell=-0.3, sf=-0.6)) )
1027.911635944512 nan 2065.762898488176 M(0, SE(ell=3.8, sf=-1.0))
1027.903814739796 nan 2065.7472560787437 M(0, SE(ell=0.0, sf=0.0))
1027.714691100425 nan 2065.3690088000017 M(0, LN(off=-8.4, ell=0.8, loc=1959.7))
1002.522711842176 nan 2024.924676882656 ( M(0, LN(off=-3.4, ell=1.0, loc=1965.1)) x M(0, PE(ell=3.3, p=0.7, sf=5.1)) )
974.4348088970949 nan 1958.8092443933417 M(0, LN(off=-15.0, ell=-1.1, loc=1967.5))
897.9109529202489 nan 1815.7011590388017 ( M(0, LN(off=-0.8, ell=-2.2, loc=1975.3)) x M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
894.5560567068537 nan 1813.9611799115874 ( M(0, PE(ell=3.4, p=1.8, sf=5.0)) + M(0, SE(ell=-0.8, sf=2.0)) )
827.4239624014712 nan 1679.6969913008222 ( M(0, PE(ell=4.6, p=1.6, sf=7.2)) x M(0, PE(ell=3.5, p=2.2, sf=4.8)) )
Printing all results
1251.6857440777076 nan 2513.311114754567 M(0, SE(ell=-0.8, sf=5.7))
1231.2193188407873 nan 2482.3178908798786 ( M(0, PE(ell=3.4, p=1.8, sf=4.6)) x M(0, SE(ell=-0.0, sf=3.7)) )
1195.301552405626 nan 2410.482358009556 ( M(0, LN(off=-2.0, ell=0.1, loc=0.0)) x M(0, PE(ell=3.4, p=1.8, sf=4.9)) )
1180.5636329338015 nan 2390.946145665059 ( M(0, PE(ell=4.6, p=6.9, sf=-2.7)) + M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1180.5492496798781 nan 2390.917379157212 ( M(0, PE(ell=1.9, p=6.9, sf=3.3)) + M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1181.7816979119368 nan 2388.4124623217535 ( M(0, LN(off=-2.0, ell=0.0, loc=0.0)) + M(0, PE(ell=3.4, p=1.8, sf=4.9)) )
1180.563652648244 nan 2385.976371794368 ( M(0, PE(ell=3.4, p=1.8, sf=5.0)) + M(0, SE(ell=0.0, sf=-0.0)) )
1180.5636337552983 nan 2385.9763340084764 ( M(0, LN(off=-11.8, ell=3.4, loc=1960.9)) + M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1180.5636331360015 nan 2385.976332769883 ( M(0, PE(ell=3.4, p=1.8, sf=5.0)) + M(0, SE(ell=0.7, sf=-2.1)) )
1180.5636329755807 nan 2385.9763324490414 ( M(0, LN(off=-13.6, ell=5.1, loc=1970.5)) + M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1181.7125696761482 nan 2383.3043925506004 ( M(0, PE(ell=3.4, p=1.8, sf=4.9)) x M(0, SE(ell=0.0, sf=-0.0)) )
1180.663863187727 nan 2376.237166274182 M(0, PE(ell=2.9, p=1.0, sf=6.3))
1181.7783896324722 nan 2373.4964058640962 M(0, LN(off=-2.0, ell=0.0, loc=0.0))
1181.7783896324722 nan 2373.4964058640962 M(0, LN(off=-2.0, ell=0.0, loc=0.0))
1181.4844478710331 nan 2372.908522341218 M(0, SE(ell=4.4, sf=3.8))
1180.6405321123773 nan 2371.2206908239064 M(0, SE(ell=3.0, sf=6.2))
1027.6078027525305 nan 2080.064672002941 ( M(0, PE(ell=3.6, p=6.3, sf=-4.3)) x M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
1027.9237881880226 nan 2070.7570162747734 M(0, PE(ell=0.0, p=1.9, sf=-5.8))
1023.6929509234078 nan 2067.2651550451196 ( M(0, PE(ell=3.4, p=1.8, sf=6.1)) x M(0, SE(ell=-0.3, sf=-0.6)) )
1027.923777964088 nan 2065.787182527328 M(0, LN(off=-7.3, ell=7.0, loc=1944.7))
1027.911635944512 nan 2065.762898488176 M(0, SE(ell=3.8, sf=-1.0))
1027.903814739796 nan 2065.7472560787437 M(0, SE(ell=0.0, sf=0.0))
1027.903814739796 nan 2065.7472560787437 M(0, SE(ell=0.0, sf=0.0))
1027.8720632958796 nan 2065.683753190911 M(0, LN(off=-8.7, ell=0.1, loc=1955.4))
1027.714691100425 nan 2065.3690088000017 M(0, LN(off=-8.4, ell=0.8, loc=1959.7))
1023.5392001361355 nan 2061.987840170999 M(0, PE(ell=1.5, p=2.2, sf=5.2))
1023.3361754305673 nan 2061.5817907598625 M(0, PE(ell=3.4, p=1.8, sf=4.9))
1002.522711842176 nan 2024.924676882656 ( M(0, LN(off=-3.4, ell=1.0, loc=1965.1)) x M(0, PE(ell=3.3, p=0.7, sf=5.1)) )
974.4348088970949 nan 1958.8092443933417 M(0, LN(off=-15.0, ell=-1.1, loc=1967.5))
897.9109529202489 nan 1815.7011590388017 ( M(0, LN(off=-0.8, ell=-2.2, loc=1975.3)) x M(0, PE(ell=3.4, p=1.8, sf=5.0)) )
894.5560567068537 nan 1813.9611799115874 ( M(0, PE(ell=3.4, p=1.8, sf=5.0)) + M(0, SE(ell=-0.8, sf=2.0)) )
827.4239624014712 nan 1679.6969913008222 ( M(0, PE(ell=4.6, p=1.6, sf=7.2)) x M(0, PE(ell=3.5, p=2.2, sf=4.8)) )
Seed kernels :
( M(0, PE(ell=4.6, p=1.6, sf=7.2)) x M(0, PE(ell=3.5, p=2.2, sf=4.8)) )
Expanded kernels :
( ( M(0, LN(off=-2.0, ell=0.0, loc=0.0)) + M(0, PE(ell=3.5, p=2.2, sf=4.8)) ) x M(0, PE(ell=4.6, p=1.6, sf=7.2)) )
( ( M(0, LN(off=-2.0, ell=0.0, loc=0.0)) + M(0, PE(ell=4.6, p=1.6, sf=7.2)) ) x M(0, PE(ell=3.5, p=2.2, sf=4.8)) )
( ( M(0, PE(ell=0.0, p=-2.0, sf=0.0)) + M(0, PE(ell=3.5, p=2.2, sf=4.8)) ) x M(0, PE(ell=4.6, p=1.6, sf=7.2)) )
( ( M(0, PE(ell=0.0, p=-2.0, sf=0.0)) + M(0, PE(ell=4.6, p=1.6, sf=7.2)) ) x M(0, PE(ell=3.5, p=2.2, sf=4.8)) )
( ( M(0, PE(ell=3.5, p=2.2, sf=4.8)) + M(0, SE(ell=0.0, sf=0.0)) ) x M(0, PE(ell=4.6, p=1.6, sf=7.2)) )
( ( M(0, PE(ell=4.6, p=1.6, sf=7.2)) + M(0, SE(ell=0.0, sf=0.0)) ) x M(0, PE(ell=3.5, p=2.2, sf=4.8)) )
( M(0, LN(off=-2.0, ell=0.0, loc=0.0)) x M(0, PE(ell=3.5, p=2.2, sf=4.8)) )
( M(0, LN(off=-2.0, ell=0.0, loc=0.0)) x M(0, PE(ell=3.5, p=2.2, sf=4.8)) x M(0, PE(ell=4.6, p=1.6, sf=7.2)) )
( M(0, LN(off=-2.0, ell=0.0, loc=0.0)) x M(0, PE(ell=4.6, p=1.6, sf=7.2)) )
( M(0, PE(ell=0.0, p=-2.0, sf=0.0)) x M(0, PE(ell=3.5, p=2.2, sf=4.8)) )
( M(0, PE(ell=0.0, p=-2.0, sf=0.0)) x M(0, PE(ell=3.5, p=2.2, sf=4.8)) x M(0, PE(ell=4.6, p=1.6, sf=7.2)) )
( M(0, PE(ell=0.0, p=-2.0, sf=0.0)) x M(0, PE(ell=4.6, p=1.6, sf=7.2)) )
( M(0, PE(ell=3.5, p=2.2, sf=4.8)) x M(0, PE(ell=4.6, p=1.6, sf=7.2)) x M(0, SE(ell=0.0, sf=0.0)) )
( M(0, PE(ell=3.5, p=2.2, sf=4.8)) x M(0, SE(ell=0.0, sf=0.0)) )
( M(0, PE(ell=4.6, p=1.6, sf=7.2)) x M(0, SE(ell=0.0, sf=0.0)) )
( ( M(0, PE(ell=3.5, p=2.2, sf=4.8)) x M(0, PE(ell=4.6, p=1.6, sf=7.2)) ) + M(0, LN(off=-2.0, ell=0.0, loc=0.0)) )
( ( M(0, PE(ell=3.5, p=2.2, sf=4.8)) x M(0, PE(ell=4.6, p=1.6, sf=7.2)) ) + M(0, PE(ell=0.0, p=-2.0, sf=0.0)) )
( ( M(0, PE(ell=3.5, p=2.2, sf=4.8)) x M(0, PE(ell=4.6, p=1.6, sf=7.2)) ) + M(0, SE(ell=0.0, sf=0.0)) )
reset: unknown terminal type unknown
Terminal type? "

Best wishes,
Chunchao