def test_make_negative_edges_check_neg_nodes(self):
unique_node_ids = list(np.unique(self.nodes.id))
neg_nodes = list(
np.unique(np.concatenate((self.ne.subject, self.ne.object))))
self.assertTrue(
set(neg_nodes) <= set(unique_node_ids),
"Some nodes from negative edges are not in the nodes tsv file")
def dice(img1, img2, labels=None, nargout=1):
'''
Dice [1] volume overlap metric
The default is to *not* return a measure for the background layer (label = 0)
[1] Dice, Lee R. "Measures of the amount of ecologic association between species."
Ecology 26.3 (1945): 297-302.
Parameters
----------
vol1 : nd array. The first volume (e.g. predicted volume)
vol2 : nd array. The second volume (e.g. "true" volume)
labels : optional vector of labels on which to compute Dice.
If this is not provided, Dice is computed on all non-background (non-0) labels
nargout : optional control of output arguments. if 1, output Dice measure(s).
if 2, output tuple of (Dice, labels)
Output
------
if nargout == 1 : dice : vector of dice measures for each labels
if nargout == 2 : (dice, labels) : where labels is a vector of the labels on which
dice was computed
'''
if labels is None:
labels = np.unique(np.concatenate((img1, img2))) # 输出一维数组
labels = np.delete(labels, np.where(labels == 0)) # remove background
dicem = np.zeros(len(labels))
for idx, lab in enumerate(labels):
top = 2 * np.sum(np.logical_and(img1 == lab, img2 == lab))
bottom = np.sum(img1 == lab) + np.sum(img2 == lab)
bottom = np.maximum(bottom, np.finfo(float).eps) # add epsilon. 机器最小的正数
dicem[idx] = top / bottom
if nargout == 1:
return dicem
else:
return (dicem, labels)
classifier = []
result = []
for el in v:
# select only with the
agent.append(el[0])
classifier.append(el[1])
result.append(el[2])
# need to order per agent
classifier_array = np.array(classifier)
agent_array = np.array(agent)
min_cla = np.amin(classifier_array)
max_cla = np.amax(classifier_array)
unique_element_classifier = np.unique(classifier_array)
# print(unique_element_classifier)
# print(len(unique_element_classifier))
unique_element_agent = np.unique(agent_array)
# print(unique_element_agent)
# print(len(unique_element_agent))
dif_cla = max_cla - min_cla
real_classifier = np.zeros(
len(unique_element_classifier))
# (maxEnd - minEnd) * ((value - minStart) / (maxStart - minStart)) + minEnd;
value_agent = agent[0]
clax = []
from sklearn.datasets import load_iris
from sklearn.datasets import make_blobs
from mglearn.datasets import load_extended_boston
from sklearn.model_selection import train_test_split
import pandas as pd
import mglearn
from IPython.display import display
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso
from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
from pandas import np
import matplotlib.pyplot as plt
X = np.array([[0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1], [1, 0, 1, 0]])
y = np.array([0, 1, 0, 1])
counts = {}
print(X.shape)
for label in np.unique(y):
# итерируем по каждому классу
# подсчитываем элементы 1 по признаку
print("y = {}".format(y))
print("label={}".format(label))
print("y == label -> {}".format(y == label))
print("X = {}".format(X[y == label]))
print("sum = {}".format(X[y == label].sum(axis=0)))
counts[label] = X[y == label].sum(axis=0)
print("Частоты признаков:\n{}".format(counts))
def compute_inertia(a, X):
W = [np.mean(pairwise_distances(X[a == c, :])) for c in np.unique(a)]
return np.mean(W)