In [1]:
from pypge.search import PGE
from pypge import expand
from pypge.benchmarks import explicit
import numpy as np
# visualization libraries
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# plot the visuals in ipython
%matplotlib inline
/Users/tony/anaconda/lib/python2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.
"`IPython.html.widgets` has moved to `ipywidgets`.", ShimWarning)
In [4]:
prob = explicit.Lipson_03(0.1)
print prob['name'], prob['eqn']
print prob['xpts'].shape
plt.plot(prob['xpts'][0], prob['ypure'], 'r.')
plt.show()
plt.plot(prob['xpts'][0], prob['ypts'], 'b.')
plt.show()
Lipson_3 x**2*exp(sin(x)) + x - sin(x**3 - 0.25*pi)
(1, 500)
In [9]:
data = np.array([prob['xpts'][0], prob['ypts']]).T
print data.shape
import json
print json.dumps(data.tolist(), indent=4)
(500, 2)
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In [10]:
pge = PGE(
system_type = "explicit",
search_vars = "y",
usable_vars = prob['xs_str'],
# usable_funcs = expand.BASIC_BASE[1:],
pop_count = 3,
peek_count = 9,
max_iter = 6
)
pge.fit(prob['xpts'], prob['ypts'])
train.shape: (1, 500) (500,)
peekn.shape: (1, 16) (16,)
Preloop setup
create first exprs: 12
filtering: 12
memoizing: 12
unique: 12 / 12
algebra: 12
filtering: 3
memoizing: 3
unique: 3 / 3
peek'n: 15
peek_queue'n: 15
peek_pop'd 9 of 15
peek_pop'd 6 of 6
eval'n: 15
eval_queue'n: 15
TOTAL 1.389 seconds
ITER: 0
eval_pop'd 3 of 15
expanding'n: 3
expanded to: 21
filtering: 21
memoizing: 20
unique: 10 / 20
algebra: 10
filtering: 1
memoizing: 1
unique: 1 / 1
peek'n: 11
peek_queue'n: 11
peek_pop'd 9 of 11
eval'n: 9
eval_queue'n: 9
Best so far
id: sz error r2 expld_vari theModel
-----------------------------------------------------------------------------------
0: 3 18421.766648 -0.716177 0.019721 2.074271*x
6: 5 10515.687410 0.020356 0.020356 1.762897*x + 88.954492
11: 15 5133.823444 0.521732 0.521732 0.021259*x**3 + 1.195609*x**2 + 5.318377
TOTAL 1.689 seconds
ITER: 1
eval_pop'd 3 of 21
expanding'n: 3
expanded to: 19
filtering: 19
memoizing: 19
unique: 10 / 19
algebra: 10
filtering: 0
memoizing: 0
unique: 0 / 0
peek'n: 10
peek_queue'n: 10
peek_pop'd 9 of 12
eval'n: 9
eval_queue'n: 9
Best so far
id: sz error r2 expld_vari theModel
-----------------------------------------------------------------------------------
0: 3 18421.766648 -0.716177 0.019721 2.074271*x
6: 5 10515.687410 0.020356 0.020356 1.762897*x + 88.954492
1: 6 5709.356989 0.468115 0.468845 1.230533*x**2
11: 15 5133.823444 0.521732 0.521732 0.021259*x**3 + 1.195609*x**2 + 5.318377
23: 17 4797.059434 0.553105 0.553105 0.000169*x**5 + 1.204629*x**2 + 5.210923
15: 5 18724.529144 -0.744382 -0.000026 1.8e-5/x
TOTAL 1.445 seconds
ITER: 2
eval_pop'd 3 of 27
expanding'n: 3
expanded to: 26
filtering: 26
memoizing: 25
unique: 12 / 25
algebra: 12
filtering: 5
memoizing: 5
unique: 3 / 5
peek'n: 15
peek_queue'n: 15
peek_pop'd 9 of 18
eval'n: 9
eval_queue'n: 9
Best so far
id: sz error r2 expld_vari theModel
-----------------------------------------------------------------------------------
0: 3 18421.766648 -0.716177 0.019721 2.074271*x
6: 5 10515.687410 0.020356 0.020356 1.762897*x + 88.954492
1: 6 5709.356989 0.468115 0.468845 1.230533*x**2
12: 7 5391.574168 0.497719 0.498181 x*(1.231252*x + 2.125112)
11: 15 5133.823444 0.521732 0.521732 0.021259*x**3 + 1.195609*x**2 + 5.318377
23: 17 4797.059434 0.553105 0.553105 0.000169*x**5 + 1.204629*x**2 + 5.210923
33: 19 4413.345522 0.588851 0.588851 1.0e-6*x**7 + 1.214658*x**2 + 4.930244
15: 5 18724.529144 -0.744382 -0.000026 1.8e-5/x
34: 6 18724.529206 -0.744383 -0.000025 0
TOTAL 2.235 seconds
ITER: 3
eval_pop'd 3 of 33
expanding'n: 3
expanded to: 25
filtering: 25
memoizing: 23
unique: 13 / 23
algebra: 13
filtering: 4
memoizing: 4
unique: 4 / 4
peek'n: 17
peek_queue'n: 17
peek_pop'd 9 of 26
eval'n: 9
eval_queue'n: 9
Best so far
id: sz error r2 expld_vari theModel
-----------------------------------------------------------------------------------
0: 3 18421.766648 -0.716177 0.019721 2.074271*x
6: 5 10515.687410 0.020356 0.020356 1.762897*x + 88.954492
1: 6 5709.356989 0.468115 0.468845 1.230533*x**2
12: 7 5391.574168 0.497719 0.498181 x*(1.231252*x + 2.125112)
11: 15 5133.823444 0.521732 0.521732 0.021259*x**3 + 1.195609*x**2 + 5.318377
23: 17 4797.059434 0.553105 0.553105 0.000169*x**5 + 1.204629*x**2 + 5.210923
33: 19 4413.345522 0.588851 0.588851 1.0e-6*x**7 + 1.214658*x**2 + 4.930244
15: 5 18724.529144 -0.744382 -0.000026 1.8e-5/x
24: 7 10718.458468 0.001465 0.001465 89.566234 - 0.002026/x
7: 8 5690.875888 0.469836 0.469836 1.176479*x**2 + 6.602704
36: 21 5019.548628 0.532377 0.532377 1.0e-6*x**7 + 0.006354*x**4 + 35.818318
34: 6 18724.529206 -0.744383 -0.000025 0
TOTAL 2.509 seconds
ITER: 4
eval_pop'd 3 of 39
expanding'n: 3
expanded to: 26
filtering: 26
memoizing: 26
unique: 16 / 26
algebra: 16
filtering: 0
memoizing: 0
unique: 0 / 0
peek'n: 16
peek_queue'n: 16
peek_pop'd 9 of 33
eval'n: 9
eval_queue'n: 9
Best so far
id: sz error r2 expld_vari theModel
-----------------------------------------------------------------------------------
0: 3 18421.766648 -0.716177 0.019721 2.074271*x
6: 5 10515.687410 0.020356 0.020356 1.762897*x + 88.954492
1: 6 5709.356989 0.468115 0.468845 1.230533*x**2
12: 7 5391.574168 0.497719 0.498181 x*(1.231252*x + 2.125112)
11: 15 5133.823444 0.521732 0.521732 0.021259*x**3 + 1.195609*x**2 + 5.318377
23: 17 4797.059434 0.553105 0.553105 0.000169*x**5 + 1.204629*x**2 + 5.210923
33: 19 4413.345522 0.588851 0.588851 1.0e-6*x**7 + 1.214658*x**2 + 4.930244
58: 27 4390.096759 0.591017 0.591017 1.0e-6*x**7 - 0.00159*x**4 + 1.489497*x**2 - 0.752191
15: 5 18724.529144 -0.744382 -0.000026 1.8e-5/x
24: 7 10718.458468 0.001465 0.001465 89.566234 - 0.002026/x
7: 8 5690.875888 0.469836 0.469836 1.176479*x**2 + 6.602704
36: 21 5019.548628 0.532377 0.532377 1.0e-6*x**7 + 0.006354*x**4 + 35.818318
34: 6 18724.529206 -0.744383 -0.000025 0
2: 7 18329.724614 -0.707602 0.037646 0.017876*x**3
26: 8 7232.034735 0.326262 0.375302 0.00773*x**4
TOTAL 2.812 seconds
ITER: 5
eval_pop'd 3 of 45
expanding'n: 3
expanded to: 32
filtering: 32
memoizing: 31
unique: 22 / 31
algebra: 22
filtering: 4
memoizing: 4
unique: 4 / 4
peek'n: 26
peek_queue'n: 26
peek_pop'd 9 of 50
eval'n: 9
eval_queue'n: 9
Best so far
id: sz error r2 expld_vari theModel
-----------------------------------------------------------------------------------
0: 3 18421.766648 -0.716177 0.019721 2.074271*x
6: 5 10515.687410 0.020356 0.020356 1.762897*x + 88.954492
1: 6 5709.356989 0.468115 0.468845 1.230533*x**2
12: 7 5391.574168 0.497719 0.498181 x*(1.231252*x + 2.125112)
11: 15 5133.823444 0.521732 0.521732 0.021259*x**3 + 1.195609*x**2 + 5.318377
23: 17 4797.059434 0.553105 0.553105 0.000169*x**5 + 1.204629*x**2 + 5.210923
33: 19 4413.345522 0.588851 0.588851 1.0e-6*x**7 + 1.214658*x**2 + 4.930244
58: 27 4390.096759 0.591017 0.591017 1.0e-6*x**7 - 0.00159*x**4 + 1.489497*x**2 - 0.752191
74: 30 4163.485837 0.612128 0.612128 2.0e-6*x**7 - 0.001734*x**4 + 1.517832*x**2 - 2.780841*x - 0.324866
15: 5 18724.529144 -0.744382 -0.000026 1.8e-5/x
24: 7 10718.458468 0.001465 0.001465 89.566234 - 0.002026/x
7: 8 5690.875888 0.469836 0.469836 1.176479*x**2 + 6.602704
36: 21 5019.548628 0.532377 0.532377 1.0e-6*x**7 + 0.006354*x**4 + 35.818318
34: 6 18724.529206 -0.744383 -0.000025 0
2: 7 18329.724614 -0.707602 0.037646 0.017876*x**3
26: 8 7232.034735 0.326262 0.375302 0.00773*x**4
35: 7 18724.529206 -0.744383 -0.000025 0
TOTAL 5.339 seconds
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-10-e76b5363af8b> in <module>()
9 )
10
---> 11 pge.fit(prob['xpts'], prob['ypts'])
/Users/tony/pypge/pypge/search.pyc in fit(self, X_train, Y_train)
120 self.set_data(X_train,Y_train) # *** see note below
121 self.preloop()
--> 122 self.loop(self.max_iter)
123 self.finalize()
124
/Users/tony/pypge/pypge/search.pyc in loop(self, iterations)
308 T.checkpoint(len(to_eval))
309
--> 310 self.print_best(24)
311
312
/Users/tony/pypge/pypge/search.pyc in print_best(self, count)
322 return
323 cnt += 1
--> 324 print " ", m
325 print ""
326
/Users/tony/pypge/pypge/model.pyc in __str__(self)
79 self.pretty_expr()
80 fs = "{:5d}: {:2d} {:15.6f} {:10.6f} {:10.6f} {:s}"
---> 81 return fs.format(self.id, self.size(),self.score,self.r2,self.evar,self.pretty)
82
83 def pretty_expr(self, float_format="%.6f"):
/Users/tony/anaconda/lib/python2.7/site-packages/sympy/core/numbers.pyc in __format__(self, format_spec)
955
956 def __format__(self, format_spec):
--> 957 return format(decimal.Decimal(str(self)), format_spec)
958
959
/Users/tony/anaconda/lib/python2.7/decimal.pyc in __format__(self, specifier, context, _localeconv)
3659 context = getcontext()
3660
-> 3661 spec = _parse_format_specifier(specifier, _localeconv=_localeconv)
3662
3663 # special values don't care about the type or precision
/Users/tony/anaconda/lib/python2.7/decimal.pyc in _parse_format_specifier(format_spec, _localeconv)
5977 m = _parse_format_specifier_regex.match(format_spec)
5978 if m is None:
-> 5979 raise ValueError("Invalid format specifier: " + format_spec)
5980
5981 # get the dictionary
ValueError: Invalid format specifier: s
In [11]:
pge.finalize()
Finalizing
Final Results
id: sz error r2 expld_vari theModel
-----------------------------------------------------------------------------------
0: 3 18421.766648 -0.716177 0.019721 2.074271*x
6: 5 10515.687410 0.020356 0.020356 1.762897*x + 88.954492
1: 6 5709.356989 0.468115 0.468845 1.230533*x**2
12: 7 5391.574168 0.497719 0.498181 x*(1.231252*x + 2.125112)
14: 10 5145.799047 0.520616 0.521088 x**2*(0.021369*x + 1.239193)
44: 13 5035.185103 0.530921 0.531526 x*(0.04395*x**2 + 1.247248*x - 3.242636)
23: 17 4797.059434 0.553105 0.553105 0.000169*x**5 + 1.204629*x**2 + 5.210923
33: 19 4413.345522 0.588851 0.588851 1.0e-6*x**7 + 1.214658*x**2 + 4.930244
77: 21 4413.345522 0.588851 0.588851 1.0e-6*x**7 + 1.214658*x**2 + 4.930246
38: 22 4191.094236 0.609556 0.609556 2.0e-6*x**7 + 1.218166*x**2 - 2.752813*x + 5.860771
74: 30 4163.485837 0.612128 0.612128 2.0e-6*x**7 - 0.001734*x**4 + 1.517832*x**2 - 2.780841*x - 0.324866
15: 5 18724.529144 -0.744382 -0.000026 1.8e-5/x
24: 7 10718.458468 0.001465 0.001465 89.566234 - 0.002026/x
7: 8 5690.875888 0.469836 0.469836 1.176479*x**2 + 6.602704
3: 10 5391.574168 0.497719 0.498181 1.231252*x**2 + 2.125112*x
9: 11 5379.867065 0.498810 0.498810 1.18817*x**2 + 2.104916*x + 5.261554
5: 14 5145.799047 0.520616 0.521088 0.021369*x**3 + 1.239193*x**2
11: 15 5133.823444 0.521732 0.521732 0.021259*x**3 + 1.195609*x**2 + 5.318377
48: 17 5035.185103 0.530921 0.531526 0.04395*x**3 + 1.247248*x**2 - 3.242636*x
18: 18 5019.806573 0.532353 0.532353 0.044191*x**3 + 1.197941*x**2 - 3.295252*x + 6.03251
29: 20 4625.564510 0.569081 0.569081 0.00028*x**5 + 1.207489*x**2 - 2.821529*x + 6.092464
58: 27 4390.096759 0.591017 0.591017 1.0e-6*x**7 - 0.00159*x**4 + 1.489497*x**2 - 0.752191
76: 28 4209.100974 0.607879 0.607879 4.0e-6*x**7 + 0.006441*x**4 - 0.073944*x**3 + 37.03974
34: 6 18724.529206 -0.744383 -0.000025 0
2: 7 18329.724614 -0.707602 0.037646 0.017876*x**3
26: 8 7232.034735 0.326262 0.375302 0.00773*x**4
21: 10 5690.875888 0.469836 0.469836 1.17648*x**2 + 6.602704
53: 13 5690.807751 0.469843 0.469843 1.176253*x**2 + 6.6303 - 0.000134/x
86: 14 5690.807340 0.469843 0.469843 1.176252*x**2 + 6.630388
22: 16 5630.231067 0.475486 0.475486 -0.002559*x**4 + 1.619517*x**2 - 2.573405
27: 19 5420.124844 0.495060 0.495060 0.000175*x**5 + 0.006282*x**4 + 35.999024
36: 21 5019.548628 0.532377 0.532377 1.0e-6*x**7 + 0.006354*x**4 + 35.818318
57: 24 4816.454223 0.551298 0.551298 2.0e-6*x**7 + 0.006362*x**4 - 2.631277*x + 36.881004
95: 26 4816.454223 0.551298 0.551298 2.0e-6*x**7 + 0.006362*x**4 - 2.631278*x + 36.881004
35: 7 18724.529206 -0.744383 -0.000025 0
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-2d0c095811b0> in <module>()
----> 1 pge.finalize()
/Users/tony/pypge/pypge/search.pyc in finalize(self, nfronts)
344 for front in final_list[:nfronts]:
345 for m in front:
--> 346 print " ", m
347 print ""
348
/Users/tony/pypge/pypge/model.pyc in __str__(self)
79 self.pretty_expr()
80 fs = "{:5d}: {:2d} {:15.6f} {:10.6f} {:10.6f} {:s}"
---> 81 return fs.format(self.id, self.size(),self.score,self.r2,self.evar,self.pretty)
82
83 def pretty_expr(self, float_format="%.6f"):
/Users/tony/anaconda/lib/python2.7/site-packages/sympy/core/numbers.pyc in __format__(self, format_spec)
955
956 def __format__(self, format_spec):
--> 957 return format(decimal.Decimal(str(self)), format_spec)
958
959
/Users/tony/anaconda/lib/python2.7/decimal.pyc in __format__(self, specifier, context, _localeconv)
3659 context = getcontext()
3660
-> 3661 spec = _parse_format_specifier(specifier, _localeconv=_localeconv)
3662
3663 # special values don't care about the type or precision
/Users/tony/anaconda/lib/python2.7/decimal.pyc in _parse_format_specifier(format_spec, _localeconv)
5977 m = _parse_format_specifier_regex.match(format_spec)
5978 if m is None:
-> 5979 raise ValueError("Invalid format specifier: " + format_spec)
5980
5981 # get the dictionary
ValueError: Invalid format specifier: s
In [16]:
final_paretos = pge.get_final_paretos()
print len(final_paretos)
final_list = [item for sublist in final_paretos for item in sublist]
print len(final_list), "\n\n"
10
69
In [15]:
pge_szs = [m.size() for m in final_list]
pge_scr = [m.score for m in final_list]
pge_evar = [1.0 - m.evar for m in final_list]
pge_szs_f = [m.size() for m in final_paretos[0]]
pge_scr_f = [m.score for m in final_paretos[0]]
pge_evar_f = [1.0 - m.evar for m in final_paretos[0]]
plt.plot(pge_szs, pge_scr, 'b.', pge_szs_f, pge_scr_f, 'ro')
plt.show()
plt.plot(pge_szs, pge_evar, 'b.', pge_szs_f, pge_evar_f, 'ro')
plt.show()
In [34]:
data_p = np.array([pge_szs, pge_evar]).T
data_pf = np.array([pge_evar_f]).T
print data_p.shape, data_pf.shape
pad_len = len(pge_szs) - len(pge_szs_f)
print "pad: ", pad_len
data_pf = np.pad(data_pf, ((0,pad_len),(0,0)), mode='constant', constant_values=0)
print data_pf.shape
all_data = np.concatenate((data_p, data_pf), 1)
print all_data.shape
print json.dumps(all_data.tolist(), indent=4)
(69, 2) (11, 1)
pad: 58
(69, 1)
(69, 3)
[
[
3.0,
0.9802794803634742,
0.9802794803634742
],
[
5.0,
0.9796444509220995,
0.9796444509220995
],
[
6.0,
0.5311554669139156,
0.5311554669139156
],
[
7.0,
0.5018193804124681,
0.5018193804124681
],
[
10.0,
0.47891175887106274,
0.47891175887106274
],
[
13.0,
0.4684738556083722,
0.4684738556083722
],
[
17.0,
0.4468954307895313,
0.4468954307895313
],
[
19.0,
0.4111485328606257,
0.4111485328606257
],
[
21.0,
0.41114853286062547,
0.41114853286062547
],
[
22.0,
0.39044353945040133,
0.39044353945040133
],
[
30.0,
0.387871533044861,
0.387871533044861
],
[
5.0,
1.0000260101169516,
0.0
],
[
7.0,
0.9985346607890436,
0.0
],
[
8.0,
0.5301636276697407,
0.0
],
[
10.0,
0.5018193838370844,
0.0
],
[
11.0,
0.5011899566328727,
0.0
],
[
14.0,
0.47891175888277293,
0.0
],
[
15.0,
0.47826846241027376,
0.0
],
[
17.0,
0.4684738549939055,
0.0
],
[
18.0,
0.46764661810572683,
0.0
],
[
20.0,
0.43091891453698694,
0.0
],
[
27.0,
0.4089826714292417,
0.0
],
[
28.0,
0.3921210522456493,
0.0
],
[
6.0,
1.0000252783923624,
0.0
],
[
7.0,
0.962354477282755,
0.0
],
[
8.0,
0.6246983673204085,
0.0
],
[
10.0,
0.5301636276697406,
0.0
],
[
13.0,
0.5301572799991721,
0.0
],
[
14.0,
0.5301572416853243,
0.0
],
[
16.0,
0.5245139387774018,
0.0
],
[
19.0,
0.5049403828907087,
0.0
],
[
21.0,
0.46762258787416133,
0.0
],
[
24.0,
0.4487022548967101,
0.0
],
[
26.0,
0.4487022548967101,
0.0
],
[
7.0,
1.0000252783327541,
0.0
],
[
8.0,
0.9985335683594552,
0.0
],
[
9.0,
0.9623493851751905,
0.0
],
[
10.0,
0.59410717651512,
0.0
],
[
16.0,
0.5301572799997577,
0.0
],
[
18.0,
0.5250346631542921,
0.0
],
[
23.0,
0.46762258787416133,
0.0
],
[
8.0,
1.0000252744740554,
0.0
],
[
9.0,
0.9796444509220996,
0.0
],
[
10.0,
0.9781927739631048,
0.0
],
[
11.0,
0.5659699951658353,
0.0
],
[
17.0,
0.5374010538675333,
0.0
],
[
20.0,
0.5257479229263499,
0.0
],
[
23.0,
0.5247490135537757,
0.0
],
[
24.0,
0.46762258787416133,
0.0
],
[
9.0,
0.9985335804813321,
0.0
],
[
11.0,
0.5892256029359428,
0.0
],
[
16.0,
0.565969995254198,
0.0
],
[
20.0,
0.540584342698616,
0.0
],
[
21.0,
0.5301572416853245,
0.0
],
[
22.0,
0.526384011132605,
0.0
],
[
31.0,
0.49705830104219473,
0.0
],
[
9.0,
0.9985346607890436,
0.0
],
[
10.0,
0.9985335804864689,
0.0
],
[
11.0,
0.9623493851752011,
0.0
],
[
12.0,
0.5892256026903442,
0.0
],
[
22.0,
0.5489798769020832,
0.0
],
[
26.0,
0.527267975948567,
0.0
],
[
9.0,
0.9381898984570916,
0.0
],
[
11.0,
0.9781923497664318,
0.0
],
[
12.0,
0.9535341170637399,
0.0
],
[
14.0,
0.9241707103674777,
0.0
],
[
10.0,
0.9586021456727991,
0.0
],
[
12.0,
0.9623493851751916,
0.0
],
[
11.0,
0.9586021493123452,
0.0
]
]
In [35]:
from pypge.evaluate import Eval
for best_m in final_paretos[0]:
print best_m
y_pred = Eval(best_m, pge.vars, prob['xpts'])
plt.plot(prob['xpts'][0], prob['ypts'], 'r.',prob['xpts'][0], y_pred, 'b.')
plt.show()
0: 3 18421.766648 -0.716177 0.019721 2.074271*x
6: 5 10515.687410 0.020356 0.020356 1.762897*x + 88.954492
1: 6 5709.356989 0.468115 0.468845 1.230533*x**2
12: 7 5391.574168 0.497719 0.498181 x*(1.231252*x + 2.125112)
14: 10 5145.799047 0.520616 0.521088 x**2*(0.021369*x + 1.239193)
44: 13 5035.185103 0.530921 0.531526 x*(0.04395*x**2 + 1.247248*x - 3.242636)
23: 17 4797.059434 0.553105 0.553105 0.000169*x**5 + 1.204629*x**2 + 5.210923
33: 19 4413.345522 0.588851 0.588851 1.0e-6*x**7 + 1.214658*x**2 + 4.930244
77: 21 4413.345522 0.588851 0.588851 1.0e-6*x**7 + 1.214658*x**2 + 4.930246
38: 22 4191.094236 0.609556 0.609556 2.0e-6*x**7 + 1.218166*x**2 - 2.752813*x + 5.860771
74: 30 4163.485837 0.612128 0.612128 2.0e-6*x**7 - 0.001734*x**4 + 1.517832*x**2 - 2.780841*x - 0.324866
In [37]:
from sympy import *
import networkx as nx
G = pge.GRAPH
n_nodes = G.number_of_nodes()
n_edges = G.number_of_edges()
print n_nodes, n_edges
print nx.info(G)
print nx.density(G)
bins = nx.degree_histogram(G)
# pos=nx.graphviz_layout(G,prog="twopi",root=pge.root_model)
# nx.draw_networkx(G,pos,with_labels=False,node_size=30)
# nx.draw_circular(G,with_labels=False,node_size=30)
# pos=nx.shell_layout(G,pge.iter_expands)
# nx.draw_networkx(G,pos,with_labels=False,node_size=30)
plt.yscale('log')
plt.bar(range(0,len(bins)),bins)
plt.draw()
111 173
Name:
Type: MultiDiGraph
Number of nodes: 111
Number of edges: 173
Average in degree: 1.5586
Average out degree: 1.5586
0.0141687141687
Content source: verdverm/pypge
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