Stars | Frequency | Percentage |
---|---|---|
5 | 246 | 96% |
4 | 8 | 3.1% |
3 | 1 | 0.4% |
2 | 1 | 0.4% |
1 | 0 | 0% |
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Ape Configuration XML Files | |
Ape configures mappers using configuration files. The standard Zope 2 | |
mapper configuration is in the file 'apeconf.xml' in the | |
'apelib.zope2' package. Refer to the standard configuration file as | |
an example. |
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Ape Configuration XML Files | |
Ape configures mappers using configuration files. The standard Zope 2 | |
mapper configuration is in the file 'apeconf.xml' in the | |
'apelib.zope2' package. Refer to the standard configuration file as | |
an example. |
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burro side: | |
local_net="192.168.2.0/26" | |
remote_net="192.168.4.0/30" | |
local_gw="186.67.181.205" | |
peer_gw="178.22.66.173" | |
state="passive" | |
ikev2 "vpn-zrh" $state esp \ | |
from $local_net to $remote_net \ | |
local $local_gw peer $peer_gw \ |
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MAX_INT = 2 ** 31 - 1 | |
ans = 0 | |
def merge(a, p, q, r): | |
left = list(a[p:q]) | |
right = list(a[q:r]) | |
for i in xrange(len(left)): | |
for j in xrange(len(right)): |
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# Project 1: Model Evaluation & Validation | |
## Predicting Boston Housing Prices | |
### Install | |
This project requires **Python 2.7** and the following Python libraries installed: | |
- [NumPy](http://www.numpy.org/) | |
- [matplotlib](http://matplotlib.org/) | |
- [scikit-learn](http://scikit-learn.org/stable/) |
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# In this exercise we'll examine a learner which has high bias, and is incapable of | |
# learning the patterns in the data. | |
# Use the learning curve function from sklearn.learning_curve to plot learning curves | |
# of both training and testing error. Use plt.plot() within the plot_curve function | |
# to create line graphs of the values. | |
from sklearn.linear_model import LinearRegression | |
from sklearn.learning_curve import learning_curve | |
import matplotlib.pyplot as plt | |
from sklearn.metrics import explained_variance_score, make_scorer |
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import peak | |
import trace | |
################################################################################ | |
################################## Algorithms ################################## | |
################################################################################ | |
def algorithm1(problem, trace = None): | |
# if it's empty, we're done | |
if problem.numRow <= 0 or problem.numCol <= 0: |
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import trace | |
################################################################################ | |
########################### Class for Peak Problems ############################ | |
################################################################################ | |
class PeakProblem(object): | |
""" | |
A class representing an instance of a peak-finding problem. | |
""" |
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