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""""""
actions = load_grouped_actions(spec, pop_keys=pop_keys)
attrs = {""actions"": actions, ""name"": name}
if ""as"" in spec:
attrs[""as_""] = spec[""as""]
if pop_keys:
del spec[""as""]
for k in (""requires"", ""methods"", ""defaults"", ""default_option""):
if k in spec:
attrs[k] = spec[k]
if pop_keys:
del spec[k]
return metaclass(name, (base_class,), attrs)"
270,"def plot_stat_summary(df, fig=None):
'''
Plot stats grouped by test capacitor load _and_ frequency.
In other words, we calculate the mean of all samples in the data
frame for each test capacitance and frequency pairing, plotting
the following stats:
- Root mean squared error
- Coefficient of variation
- Bias
## [Coefficient of variation][1] ##
> In probability theory and statistics, the coefficient of
> variation (CV) is a normalized measure of dispersion of a
> probability distribution or frequency distribution. It is defined
> as the ratio of the standard deviation to the mean.
[1]: http://en.wikipedia.org/wiki/Coefficient_of_variation
'''
if fig is None:
fig = plt.figure(figsize=(8, 8))
# Define a subplot layout, 3 rows, 2 columns
grid = GridSpec(3, 2)
stats = calculate_stats(df, groupby=['test_capacitor',
'frequency']).dropna()
for i, stat in enumerate(['RMSE %', 'cv %', 'bias %']):
axis = fig.add_subplot(grid[i, 0])
axis.set_title(stat)
# Plot a colormap to show how the statistical value changes
# according to frequency/capacitance pairs.
plot_colormap(stats, stat, axis=axis, fig=fig)
axis = fig.add_subplot(grid[i, 1])
axis.set_title(stat)
# Plot a histogram to show the distribution of statistical
# values across all frequency/capacitance pairs.
try:
axis.hist(stats[stat].values, bins=50)
except AttributeError:
print stats[stat].describe()
fig.tight_layout()"
271,"def calculate_inverse_document_frequencies(self):
""""""Q.calculate_inverse_document_frequencies() -- measures how much
information the term provides, i.e. whether the term is common or
rare across all documents.
This is obtained by dividing the total number of documents
by the number of documents containing the term,
and then taking the logarithm of that quotient.
""""""
for doc in self.processed_corpus:
for word in doc:
self.inverse_document_frequencies[word] += 1
for key,value in self.inverse_document_frequencies.iteritems():
idf = log((1.0 * len(self.corpus)) / value)
self.inverse_document_frequencies[key] = idf"
272,"def calculate_term_frequencies(self):
""""""Q.calculate_term_frequencies() -- calculate the number of times
each term t occurs in document d.
""""""
for doc in self.processed_corpus:
term_frequency_doc = defaultdict(int)
for word in doc:
term_frequency_doc[word] += 1
for key,value in term_frequency_doc.iteritems():
term_frequency_doc[key] = (1.0 * value) / len(doc)
self.term_frequencies.append(term_frequency_doc)"
273,"def match_query_to_corpus(self):
""""""Q.match_query_to_corpus() -> index -- return the matched corpus
index of the user query
""""""
ranking = []
for i,doc in enumerate(self.processed_corpus):
rank = 0.0
for word in self.processed_query:
if word in doc:
rank += self.term_frequencies[i][word] * self.inverse_document_frequencies[word]
ranking.append((rank,i))
matching_corpus_index = 0
max_rank = 0
for rank,index in ranking:
if rank > max_rank:
matching_corpus_index = index