def setTpTw(data, realStepTime, tpValue, twValue, technique):
"""
setTpTw is simple function to process RAW data form electrochemical
analyzer. I can process data from SCV, DPV and NPV techniques.
sig - signal with raw data (readout from A/D converter)
realStepTime - total probing time of one step (i.e. in SCV it is tp, in DPV and
NPV it is 2*tp
tpValue - new tp time
twValue - new wait time (tp + tw =< realStepTime)
technique - type of technique: 'sc', 'scv', 'dp', 'dpv' 'dpasv', 'dpas' ,
'np', 'npv', 'npasv', 'sqw', 'swv'
if returns touple of
( finalVector, onPulseVector, onStepVector)
"""
#TODO: preallocate arrays ?
sumtptw = twValue + tpValue
assert (realStepTime >= sumtptw)
averagedTmp = []
scvLike = ['sc', 'scv']
pulseLike = ['dp', 'dpv', 'np', 'npv']
sqwLike = ['sqw', 'swv']
for i in np.arange(0, len(data), realStepTime):
st = (i + twValue)
end = st + tpValue
averagedTmp.append(np.mean(data[st:end]))
if technique in scvLike:
return averagedTmp, averagedTmp, averagedTmp
elif technique in pulseLike:
res = []
partial1 = []
partial2 = []
for i in np.arange(0, len(averagedTmp) - 1, 2):
i = int(i)
res.append(averagedTmp[i + 1] - averagedTmp[i])
partial1.append(averagedTmp[i])
partial2.append(averagedTmp[i + 1])
return res, partial1, partial2
elif technique in sqwLike:
res = []
partial1 = []
partial2 = []
for i in np.arage(0, len(averagedTmp) - 1, 2):
i = int(i)
res.append(averagedTmp[i] - averagedTmp[i + 1])
partial1.append(averagedTmp[i + 1])
partial2.append(averagedTmp[i])
return res, partial1, partial2
else:
raise LookupError('Unknown technique: %s' % technique)
def backward(self, gradient=1):
batch_size = self.y.shape[0]
if self.y.size == self.y_hat.size:
dx = (self.y_hat - self.y)/batch_size
else:
dx = self.y_hat.copy()
dx[np.arage(batch_size), self.y]-=1
dx = dx/batch_size
return dx# -*- coding: utf-8 -*-
def __init__(self, cMats, n_macro=2, nProc=1):
check_matrices_shape(cMats)
self.n_models = len(cMats)
self.n_macro = 2
self.n_micro = cMats[0].shape[0]
self.uncombined = np.ones(self.n_micro, dtype=np.int32)
self.stateskeep = np.arange(self.n_micro, dtype=np.int32)
self.unmerged = np.ones(self.n_micro, dtype=np.int32)
self.indices = np.arange(self.n_micro, dtype=np.int32)
self.map = np.arage(self.n_micro, dtype=np.int32)
self.pseud = np.ones(self.n_micro, dtype=np.float32) / self.n_micro
self.nProc = nProc
def create_bar(x,
title=None,
xlabel=None,
ylabel=None,
ticks='',
size=(6, 4),
axis='v'):
'''Create a bar chart using matplotlib and the passed arguments
x - List: Series data to be plotted
Title, xlabel, ylabel - Str: Text for the figure title and axes. Axes are reversed for horionztal charts
Size - Tuple: Two element tuple defining the width and height of the figure
Axis - Str: h for horizontal bar charts, y for vertical. Default = vertical
Code for adding bar labels taken from stack overflow
https://stackoverflow.com/questions/30228069/how-to-display-the-value-of-the-bar-on-each-bar-with-pyplot-barh'''
mpl.style.use('ggplot')
plt.figure(figsize=size)
if axis == 'h':
plt.barh(np.arange(1, len(x) + 1), x, tick_label=ticks)
plt.xlabel(ylabel, fontsize=10)
plt.ylabel(xlabel, fontsize=10)
for i, v in enumerate(x):
plt.text(v + 0.02,
i + .9,
str(v),
color='blue',
fontweight='bold')
else:
plt.bar(np.arage(1, len(x) + 1), x, tick_label=ticks)
plt.xlabel(xlabel, fontsize=10)
plt.ylabel(ylabel, fontsize=10)
plt.title(title, fontsize=14)
plt.xticks(fontsize=8)
plt.yticks(fontsize=8)
plt.show()
kpca = KernelPCA(n_components = 2, kernel = 'rbf')
X_train = kpca.fit_transform(X_train)
X_test = kpca.transform(X_test)
#Fitting Logistic regression to the Training set
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifer.fit(X_train, y_train)
#Predicting the Test set results
y_pred = classifier.predict(X_test)
#Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
#Visualizing the Training set results (use this to see test set results by changing the variable)
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrind(np.arage(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01), np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Logistic Regression (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
import numpy as np
import matplotlib.pylab as plt
def sigmoid(x): # 시그모이드 함수
return 1 / (1 + np.exp(-x))
def relu(x): # 렐루라고 부름. p.76 ReLU
return np.maximum(0, x)
x = np.array([-1.0, 1.0, 2.0])
print(sigmoid(x))
t = np.array([1.0, 2.0, 3.0])
print(1.0 + t)
print(1.0 / t)
x = np.arage(-5.0, 5.0, 0.1)
y = sigmoid(x)
plt.plot(x, y)
plt.ylim(-0.1, 1.1) # y축 범위 지정
#plt.show()
# 정교한 값이 필요하여 계단함수보다 시그모이드 함수를 좀 더 많이 사용함(현재)
print v # In[4]: 2*v # In[5]: import numpy as np # In[6]: v=np.arage(1,6) # In[7]: v=np.arange(1,6) # In[8]: v # In[9]: 2*v
def _get_edge(self):
if self.dataset == 'kinetics':
num_node = 18
neighbor_link = [(4, 3), (3, 2), (7, 6), (6, 5),
(13, 12), (12, 11), (10, 9), (9, 8), (11, 5),
(8, 2), (5, 1), (2, 1), (0, 1), (15, 0), (14, 0),
(17, 15), (16, 14), (8, 11)]
connect_joint = np.array(
[1, 1, 1, 2, 3, 1, 5, 6, 2, 8, 9, 5, 11, 12, 0, 0, 14, 15])
parts = [
np.array([5, 6, 7]), # left_arm
np.array([2, 3, 4]), # right_arm
np.array([11, 12, 13]), # left_leg
np.array([8, 9, 10]), # right_leg
np.array([0, 1, 14, 15, 16, 17]) # torso
]
elif self.dataset == 'ntu':
num_node = 25
neighbor_1base = [(1, 2), (2, 21), (3, 21),
(4, 3), (5, 21), (6, 5), (7, 6), (8, 7), (9, 21),
(10, 9), (11, 10), (12, 11), (13, 1), (14, 13),
(15, 14), (16, 15), (17, 1), (18, 17), (19, 18),
(20, 19), (22, 23), (23, 8), (24, 25), (25, 12)]
neighbor_link = [(i - 1, j - 1) for (i, j) in neighbor_1base]
connect_joint = np.array([
2, 2, 21, 3, 21, 5, 6, 7, 21, 9, 10, 11, 1, 13, 14, 15, 1, 17,
18, 19, 2, 23, 8, 25, 12
]) - 1
parts = [
np.array([5, 6, 7, 8, 22, 23]) - 1, # left_arm
np.array([9, 10, 11, 12, 24, 25]) - 1, # right_arm
np.array([13, 14, 15, 16]) - 1, # left_leg
np.array([17, 18, 19, 20]) - 1, # right_leg
np.array([1, 2, 3, 4, 21]) - 1 # torso
]
elif self.dataset == 'ntu_ax':
num_node = 118
neighbor_1base = [
(1, 2),
(3, 2),
(4, 3),
(5, 4),
(6, 2),
(7, 6),
(8, 7),
(9, 2),
(10, 9),
(11, 10),
(12, 11),
(13, 9),
(14, 13),
(15, 14),
(16, 1),
(17, 1),
(18, 16),
(19, 17),
(20, 15),
(21, 20),
(22, 15),
(23, 12),
(24, 23),
(25, 12),
(26, 5),
(27, 26),
(28, 27),
(29, 28),
(30, 29),
(31, 26), # Left hand coordinates
(32, 31),
(33, 32),
(34, 33),
(35, 26),
(36, 35),
(37, 36),
(38, 37),
(39, 26),
(40, 39),
(41, 40),
(42, 41),
(43, 26),
(44, 43),
(45, 44),
(46, 45),
(47, 8),
(48, 47),
(49, 48), # Right hand coordinates
(50, 49),
(51, 50),
(52, 47),
(53, 52),
(54, 53),
(55, 54),
(56, 47),
(57, 56),
(58, 57),
(59, 58),
(60, 47),
(61, 60),
(62, 61),
(63, 62),
(64, 47),
(65, 64),
(66, 65),
(67, 66),
(68, 69),
(69, 70),
(70, 71),
(71, 72),
(73, 74),
(74, 75), # Face coordinates
(75, 76),
(76, 77),
(78, 1),
(79, 78),
(80, 79),
(81, 80),
(82, 83),
(83, 84),
(84, 85),
(85, 86),
(87, 16),
(88, 87),
(89, 88),
(90, 89),
(91, 90),
(92, 91),
(92, 87),
(93, 17),
(94, 93),
(95, 94),
(96, 95),
(97, 96),
(98, 97),
(98, 93),
(99, 100),
(100, 101),
(101, 102),
(102, 103),
(103, 104),
(104, 105),
(105, 106),
(106, 107),
(107, 108),
(108, 109),
(109, 110),
(110, 99),
(111, 112),
(112, 113),
(113, 114),
(114, 115),
(115, 116),
(116, 117),
(117, 118),
(118, 111)
]
neighbor_link = [(i - 1, j - 1) for (i, j) in neighbor_1base]
connect_joint = np.array([
2, 2, 21, 3, 21, 5, 6, 7, 21, 9, 10, 11, 1, 13, 14, 15, 1, 17,
18, 19, 2, 23, 8, 25, 12
]) - 1
parts = [
np.array([5, 6, 7]), # left_arm
np.array([2, 3, 4]), # right_arm
np.array([12, 13, 14, 19, 20, 21]), # left_leg
np.array([9, 10, 11, 22, 23, 24]), # right_leg
np.array([1, 8]), # torso
np.array([0, 15, 16, 17, 18]), # head
np.arange(21) + 25, # left fingers
np.arange(21) + 46, # right fingers
np.arage(51) + 67 # face
]
elif self.dataset == 'ntu_hx':
num_node = 67
neighbor_1base = [
(1, 2),
(3, 2),
(4, 3),
(5, 4),
(6, 2),
(7, 6),
(8, 7),
(9, 2),
(10, 9),
(11, 10),
(12, 11),
(13, 9),
(14, 13),
(15, 14),
(16, 1),
(17, 1),
(18, 16),
(19, 17),
(20, 15),
(21, 20),
(22, 15),
(23, 12),
(24, 23),
(25, 12),
(26, 5),
(27, 26),
(28, 27),
(29, 28),
(30, 29),
(31, 26), # Left hand coordinates
(32, 31),
(33, 32),
(34, 33),
(35, 26),
(36, 35),
(37, 36),
(38, 37),
(39, 26),
(40, 39),
(41, 40),
(42, 41),
(43, 26),
(44, 43),
(45, 44),
(46, 45),
(47, 8),
(48, 47),
(49, 48), # Right hand coordinates
(50, 49),
(51, 50),
(52, 47),
(53, 52),
(54, 53),
(55, 54),
(56, 47),
(57, 56),
(58, 57),
(59, 58),
(60, 47),
(61, 60),
(62, 61),
(63, 62),
(64, 47),
(65, 64),
(66, 65),
(67, 66)
]
neighbor_link = [(i - 1, j - 1) for (i, j) in neighbor_1base]
connect_joint = np.array([
2, 2, 21, 3, 21, 5, 6, 7, 21, 9, 10, 11, 1, 13, 14, 15, 1, 17,
18, 19, 2, 23, 8, 25, 12
]) - 1
parts = [
np.array([5, 6, 7]), # left_arm
np.array([2, 3, 4]), # right_arm
np.array([12, 13, 14, 19, 20, 21]), # left_leg
np.array([9, 10, 11, 22, 23, 24]), # right_leg
np.array([1, 8]), # torso
np.array([0, 15, 16, 17, 18]), # head
np.arange(21) + 25, # left fingers
np.arange(21) + 46, # right fingers
]
elif self.dataset == 'sysu':
num_node = 20
neighbor_1base = [(1, 2), (2, 3), (3, 4), (3, 5), (5, 6), (6, 7),
(7, 8), (3, 9), (9, 10), (10, 11), (11, 12),
(1, 13), (13, 14), (14, 15), (15, 16), (1, 17),
(17, 18), (18, 19), (19, 20)]
neighbor_link = [(i - 1, j - 1) for (i, j) in neighbor_1base]
connect_joint = np.array([
2, 2, 2, 3, 3, 5, 6, 7, 3, 9, 10, 11, 1, 13, 14, 15, 1, 17, 18,
19
]) - 1
parts = [
np.array([5, 6, 7, 8]) - 1, # left_arm
np.array([9, 10, 11, 12]) - 1, # right_arm
np.array([13, 14, 15, 16]) - 1, # left_leg
np.array([17, 18, 19, 20]) - 1, # right_leg
np.array([1, 2, 3, 4]) - 1 # torso
]
elif self.dataset == 'ucla':
num_node = 20
neighbor_1base = [(1, 2), (2, 3), (3, 4), (3, 5), (5, 6), (6, 7),
(7, 8), (3, 9), (9, 10), (10, 11), (11, 12),
(1, 13), (13, 14), (14, 15), (15, 16), (1, 17),
(17, 18), (18, 19), (19, 20)]
neighbor_link = [(i - 1, j - 1) for (i, j) in neighbor_1base]
connect_joint = np.array([
2, 2, 2, 3, 3, 5, 6, 7, 3, 9, 10, 11, 1, 13, 14, 15, 1, 17, 18,
19
]) - 1
parts = [
np.array([5, 6, 7, 8]) - 1, # left_arm
np.array([9, 10, 11, 12]) - 1, # right_arm
np.array([13, 14, 15, 16]) - 1, # left_leg
np.array([17, 18, 19, 20]) - 1, # right_leg
np.array([1, 2, 3, 4]) - 1 # torso
]
elif self.dataset == 'cmu':
num_node = 26
neighbor_1base = [(1, 2), (2, 3), (3, 4), (5, 6), (6, 7), (7, 8),
(1, 9), (5, 9), (9, 10), (10, 11), (11, 12),
(12, 13), (13, 14), (12, 15), (15, 16), (16, 17),
(17, 18), (18, 19), (17, 20), (12, 21), (21, 22),
(22, 23), (23, 24), (24, 25), (23, 26)]
neighbor_link = [(i - 1, j - 1) for (i, j) in neighbor_1base]
connect_joint = np.array([
9, 1, 2, 3, 9, 5, 6, 7, 10, 10, 10, 11, 12, 13, 12, 15, 16, 17,
18, 17, 12, 21, 22, 23, 24, 23
]) - 1
parts = [
np.array([15, 16, 17, 18, 19, 20]) - 1, # left_arm
np.array([21, 22, 23, 24, 25, 26]) - 1, # right_arm
np.array([1, 2, 3, 4]) - 1, # left_leg
np.array([5, 6, 7, 8]) - 1, # right_leg
np.array([9, 10, 11, 12, 13, 14]) - 1 # torso
]
elif self.dataset == 'h36m':
num_node = 20
neighbor_1base = [(1, 2), (2, 3), (3, 4), (5, 6), (6, 7), (7, 8),
(1, 9), (5, 9), (9, 10), (10, 11), (11, 12),
(10, 13), (13, 14), (14, 15), (15, 16), (10, 17),
(17, 18), (18, 19), (19, 20)]
neighbor_link = [(i - 1, j - 1) for (i, j) in neighbor_1base]
connect_joint = np.array([
9, 1, 2, 3, 9, 5, 6, 7, 9, 9, 10, 11, 10, 13, 14, 15, 10, 17,
18, 19
]) - 1
parts = [
np.array([13, 14, 15, 16]) - 1, # left_arm
np.array([17, 18, 19, 20]) - 1, # right_arm
np.array([1, 2, 3, 4]) - 1, # left_leg
np.array([5, 6, 7, 8]) - 1, # right_leg
np.array([9, 10, 11, 12]) - 1 # torso
]
else:
num_node, neighbor_link, connect_joint, parts = 0, [], [], []
logging.info('')
logging.error('Error: Do NOT exist this dataset: {}!'.format(
self.dataset))
raise ValueError()
self_link = [(i, i) for i in range(num_node)]
edge = self_link + neighbor_link
return num_node, edge, connect_joint, parts
# Example1 # import numpy as np # a = np.arange(20) # s = slice(2, 20, 2) # print(a[s]) # Example2 import numpy as np a = np.arage(10) b = a[2:7:2] print(b)