本文整理汇总了Python中Orange.data.Table.concatenate方法的典型用法代码示例。如果您正苦于以下问题:Python Table.concatenate方法的具体用法?Python Table.concatenate怎么用?Python Table.concatenate使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Orange.data.Table
的用法示例。
在下文中一共展示了Table.concatenate方法的8个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: add_data
# 需要导入模块: from Orange.data import Table [as 别名]
# 或者: from Orange.data.Table import concatenate [as 别名]
def add_data(self, time=0.4):
if self.data and len(self.data) > 2000:
return self.__timer.stop()
data_sample = self.sql_data.sample_time(time, no_cache=True)
if data_sample:
data_sample.download_data(2000, partial=True)
data = Table(data_sample)
self.data = Table.concatenate((self.data, data), axis=0)
self.handleNewSignals()
示例2: compute_distances
# 需要导入模块: from Orange.data import Table [as 别名]
# 或者: from Orange.data.Table import concatenate [as 别名]
def compute_distances(self):
self.Error.diff_domains.clear()
if self.data is None or len(self.data) == 0 \
or self.reference is None or len(self.reference) == 0:
self.distances = None
return
if self.reference.domain != self.data.domain:
self.Error.diff_domains()
self.distances = None
return
distance = METRICS[self.distance_index][1]
n_ref = len(self.reference)
all_data = Table.concatenate([self.reference, self.data], 0)
pp_all_data = Impute()(RemoveNaNColumns()(all_data))
pp_reference, pp_data = pp_all_data[:n_ref], pp_all_data[n_ref:]
self.distances = distance(pp_data, pp_reference).min(axis=1)
示例3: add_results_to_items
# 需要导入模块: from Orange.data import Table [as 别名]
# 或者: from Orange.data.Table import concatenate [as 别名]
def add_results_to_items(G, lblhistory):
items = G.items()
if items is not None and CLUSTERING_LABEL in items.domain:
domain = Domain([a for a in items.domain.attributes
if a.name != CLUSTERING_LABEL],
items.domain.class_vars,
items.domain.metas)
items = Table.from_table(domain, items)
attrs = [DiscreteVariable(CLUSTERING_LABEL,
values=list(set([l for l in lblhistory[-1]])))]
domain = Domain(attrs)
data = Table(domain, [[l] for l in lblhistory[-1]])
if items is None:
G.set_items(data)
else:
G.set_items(Table.concatenate((items, data)))
示例4: apply
# 需要导入模块: from Orange.data import Table [as 别名]
# 或者: from Orange.data.Table import concatenate [as 别名]
def apply(self):
if self.data is None or self.reference is None:
self.send("Neighbors", None)
return
distance = METRICS[self.distance_index][1]
n_data, n_ref = len(self.data), len(self.reference)
all_data = Table.concatenate([self.reference, self.data], 0)
pp_all_data = Impute()(RemoveNaNColumns()(all_data))
pp_data, pp_reference = pp_all_data[n_ref:], pp_all_data[:n_ref]
dist = distance(np.vstack((pp_data, pp_reference)))[:n_data, n_data:]
data = self._add_similarity(self.data, dist)
sorted_indices = list(np.argsort(dist.flatten()))[::-1]
indices = []
while len(sorted_indices) > 0 and len(indices) < self.n_neighbors:
index = int(sorted_indices.pop() / len(self.reference))
if (self.data[index] not in self.reference or
not self.exclude_reference) and index not in indices:
indices.append(index)
neighbors = data[indices]
neighbors.attributes = self.data.attributes
self.send("Neighbors", neighbors)
示例5: add_history_to_items
# 需要导入模块: from Orange.data import Table [as 别名]
# 或者: from Orange.data.Table import concatenate [as 别名]
def add_history_to_items(G, lblhistory):
items = G.items()
if items is not None:
domain = Domain([a for a in items.domain.attributes
if not _is_history_attr(a.name)],
items.domain.class_vars,
items.domain.metas)
items = Table.from_table(domain, items)
attrs = [DiscreteVariable('c' + str(i),
values=list(set(lblhistory[0])))
for i in range(len(lblhistory))]
domain = Domain(attrs)
# transpose history
data = [list(i) for i in zip(*lblhistory)]
data = Table(domain, data)
if items is None:
G.set_items(data)
else:
G.set_items(Table.concatenate((items, data)))
示例6: send_data
# 需要导入模块: from Orange.data import Table [as 别名]
# 或者: from Orange.data.Table import concatenate [as 别名]
def send_data(self):
if len(self.job_queue) <= 0 and len(self.job_working) <= 0:
self.btnCommit.setChecked(False)
self.btnStopA.setEnabled(False)
if self.analdata is not None and len(self.analdata) > 0 and \
len(self.analfeatures) > 0:
vars = []
analdata = []
for name, var in self.analfeatures:
analdata.append(self.analdata[name])
vars.append(var)
table = Table(Domain(vars),
[list(t) for t in zip(*analdata)])
if self.items_analysis:
table = Table.concatenate((table, self.items_analysis))
self.graph.set_items(table)
self.send("Network", self.graph)
self.send("Items", self.graph.items())
self.clear_results()
示例7: compute_distances
# 需要导入模块: from Orange.data import Table [as 别名]
# 或者: from Orange.data.Table import concatenate [as 别名]
def compute_distances(self):
self.Error.diff_domains.clear()
if not self.data or not self.reference:
self.distances = None
return
if set(self.reference.domain.attributes) != \
set(self.data.domain.attributes):
self.Error.diff_domains()
self.distances = None
return
metric = METRICS[self.distance_index][1]
n_ref = len(self.reference)
# comparing only attributes, no metas and class-vars
new_domain = Domain(self.data.domain.attributes)
reference = self.reference.transform(new_domain)
data = self.data.transform(new_domain)
all_data = Table.concatenate([reference, data], 0)
pp_all_data = Impute()(RemoveNaNColumns()(all_data))
pp_reference, pp_data = pp_all_data[:n_ref], pp_all_data[n_ref:]
self.distances = metric(pp_data, pp_reference).min(axis=1)
示例8: test
# 需要导入模块: from Orange.data import Table [as 别名]
# 或者: from Orange.data.Table import concatenate [as 别名]
#.........这里部分代码省略.........
# print()
# for i in range(10, 11):
# lac_scores = [s for k, s in all_lac_scores if k == i]
# print("lac, %i, %f, %f, %f, %f" % (i, min(lac_scores or [0]), min([l for l in lac_scores if l] or [0]), max(lac_scores or [0]), sum([l for l in lac_scores if l] or [0]) / len([l for l in lac_scores if l] or [0])))
realk = max(k for k, s in all_lac_scores)
#if lac.k < 2:
# continue
try:
lac = LAC(ds, realk)
km = KM(ds.X, realk)
gmm = GMM(ds.X, realk)
except:
print("Error")
continue
lac_score = scorer(lac, ds.X)
opts = dict(defined_=lac_score.defined) if hasattr(lac_score, 'defined') else {}
km_score = scorer(km, ds.X)
km_score_d = scorer(km, ds.X, **opts)
gmm_score = scorer(gmm, ds.X)
gmm_score_d = scorer(gmm, ds.X, **opts)
knn_score = LouAUC(ds)
results.append((km_score, gmm_score, lac_score))
if not print_latex:
print("dataset: %s (%s rows, %s features)" % (ds.name, len(ds), len(ds.domain)))
print("normalization: ", normalization)
print("reorder: ", reorder)
print("scoring function: ", score)
print("----------------")
print("k-means: %.5f" % km_score)
print("gmm: %.5f" % gmm_score)
print("k-means (dropout): %.5f" % km_score_d)
print("gmm (dropout): %.5f" % gmm_score_d)
print("lac: %.5f" % lac_score)
print("----------------")
print("knn AUC (lou) %.5f" % knn_score)
print("----------------")
print("k=%s, dropout %s (%.1f%%)" % (realk, sum(~lac_score.defined), (sum(~lac_score.defined) / len(
ds.X)) *
100))
print()
print()
# print("%s,%s,%s,%s,%f,%f,%f,%i,%f,%f" % (normalization, reorder, score, ds.name, km_score, gmm_score,
# lac_score, realk, sum(~lac_score.defined), sum(~lac_score.defined) /
# len(ds.X)))
w1, _ = get_cluster_weights(lac.priors, lac.means, lac.covars, ds.X, crisp=False)
w2, _ = get_cluster_weights(lac.priors, lac.means, lac.covars, ds.X, crisp=True)
w2 = np.argmax(w2, axis=1)[:, None]
domain = Domain([ContinuousVariable("p%d" % i) for i in range(w1.shape[1])])
probs = Table(domain, w1)
labels = Table(Domain([DiscreteVariable("label", values=list(range(k)))]), w2)
ds2.name = ds2.name.replace("/", "_")
ds.name = ds2.name.replace("/", "_")
tbl = Table.concatenate((ds, probs, labels))
tbl.save(os.path.join('output', ds2.name + ".lac.tab"))
def annotate(minis):
def _annotate(ax):
for m in minis:
ax.plot([m-1, m, m+1], [.5, .5, .5])
return _annotate
parallel_coordinates_plot(ds.name + ".kmeans.pdf", ds.X,
means=km.means, stdevs=np.sqrt(km.covars), annotate=annotate(km.minis))
parallel_coordinates_plot(ds.name + ".lac.pdf", ds.X,
means=lac.means, stdevs=np.sqrt(lac.covars), annotate=annotate(lac.minis))
parallel_coordinates_plot(ds.name + ".gmm.pdf", ds.X,
means=gmm.means, stdevs=np.sqrt(gmm.covars), annotate=annotate(gmm.minis))
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import math
#iris = Table("wine")
#for m in range(lac.means.shape[1]):
# plt.clf()
# for p, mean, variance, c in zip(lac.priors, lac.means[:, m], lac.covars[:, m], "grb"):
# sigma = math.sqrt(variance)
# x = np.linspace(0,1,100)
# plt.plot(x,p * mlab.normpdf(x, mean,sigma), color=c)
# plt.plot(ds.X[:, m].ravel() + np.random.random(len(ds)) * 0.02, [0.05]*len(ds.X) + iris.Y.ravel() * 0.1,
# "k|")
# plt.ylabel("pdf")
# plt.xlabel(iris.domain[m].name)
# plt.savefig("axis-%d.pdf" % m)
if print_latex:
print(r"\end{tabular}")
results = np.array(results)