def intersect(plane, ray_pos, rays):
"""Returns, as an array of triples, the x-y-z coordinates of the intersections
of rays originating at ray_pos with plane. Rays are given as a matrix of column
vectors"""
nt = plane.normal.T
rel = (np.array(rays) * ((nt * (plane.pos - ray_pos))[0, 0] / np.array(nt * rays)[0])).transpose()
return np.array(ray_pos.transpose())[0] + rel
def local_trans(self, joint_name, joint_angle):
'''calculate local transformation of one joint
:param str joint_name: the name of joint
:param float joint_angle: the angle of joint in radians
:return: transformation
:rtype: 4x4 matrix
'''
T = identity(4)
# YOUR CODE HERE
s = sin(joint_angle)
c = cos(joint_angle)
# X
if 'Roll' in joint_name:
T = array([[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0,
1]])
# Y
if 'Pitch' in joint_name:
T = array([[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0,
1]])
# Z
if 'Yaw' in joint_name:
T = array([[c, s, 0, 0], [-s, c, 0, 0], [0, 0, 1, 0], [0, 0, 0,
1]])
T[0:3, 3] = self.joint_length[joint_name]
return T
def findLoudestRegion(segments,tempos):
segmentMarkers = []
for segs,tempo in zip(segments,tempos):
LOUDNESS_CEILING = .8
LOUDNESS_FLOOR = 1.2
window = int((16.0/tempo)*60.0/(matlib.mean(matlib.array(segs.durations))))
lpf = np.convolve(segs.loudness_max,np.ones(window)/window)[window/2:-(window/2)]
lpf[0:window/2] = lpf[window/2]
lpf[-(window/2):] = lpf[-(window/2)]
mean = matlib.mean(matlib.array(lpf))
marker1 = 0
finalMarkers = (0,0)
foundFirstMarker = 0
while((sum([s.duration for s in segs[finalMarkers[0]:finalMarkers[1]]]) < 60.0)
and LOUDNESS_FLOOR < 2.0):
for i,l in enumerate(lpf):
if(foundFirstMarker):
if l < mean*LOUDNESS_FLOOR or i == len(lpf)-1:
foundFirstMarker = 0
if((i-marker1) > (finalMarkers[1]-finalMarkers[0])):
finalMarkers = (marker1,i)
elif l > mean*LOUDNESS_CEILING:
foundFirstMarker = 1
marker1 = i
# adjust thresholds to allow for longer region to be chosen, if necessary
LOUDNESS_FLOOR = LOUDNESS_FLOOR + .05
LOUDNESS_CEILING = LOUDNESS_CEILING + .05
segmentMarkers.append(finalMarkers)
return segmentMarkers
def fetch_pids_ttol(pnts: MatrixVector, psys: PanelSystem, ztol: float=0.01, ttol: float=0.1):
shp = pnts.shape
pnts = pnts.reshape((-1, 1))
numpnt = pnts.shape[0]
numpnl = len(psys.pnls)
pidm = zeros((1, numpnl), dtype=int)
wintm = zeros((numpnt, numpnl), dtype=bool)
abszm = zeros((numpnt, numpnl), dtype=float)
for pnl in psys.pnls.values():
pidm[0, pnl.ind] = pnl.pid
wintm[:, pnl.ind], abszm[:, pnl.ind] = pnl.within_and_absz_ttol(pnts[:, 0], ttol=ttol)
abszm[wintm is False] = float('inf')
minm = argmin(abszm, axis=1)
minm = array(minm).flatten()
pidm = array(pidm).flatten()
pids = pidm[minm]
pids = matrix([pids], dtype=int).transpose()
indp = arange(numpnt)
minz = array(abszm[indp, minm]).flatten()
minz = matrix([minz], dtype=float).transpose()
chkz = minz < ztol
pids[logical_not(chkz)] = 0
pids = pids.reshape(shp)
chkz = chkz.reshape(shp)
return pids, chkz
def calc_phi_points(points, laserpos, lasertheta):
"""Given an array of triples points that should be in the plane generated by a
laser at laserpos with theta lasertheta, calculate the inclination of the laser
plane's normal vector."""
plane_line = ddd.coord(-np.sin(lasertheta), np.cos(lasertheta), 0)
normals = np.cross(np.array(plane_line.T)[0], points - np.array(laserpos.T)[0])
return calc_phi_norm(np.average((normals.T / npl.norm(normals, axis = 1)).T, axis = 0), lasertheta)
def local_trans(self, joint_name, joint_angle):
'''calculate local transformation of one joint
:param str joint_name: the name of joint
:param float joint_angle: the angle of joint in radians
:return: transformation
:rtype: 4x4 matrix
'''
#T = array((4,4))
s = sin(joint_angle)
c = cos(joint_angle)
#x-Rotation
if joint_name in ['LShoulderRoll','LElbowRoll','RShoulderRoll','RElbowRoll','LHipRoll','LAnkleRoll','RHipRoll','RAnkleRoll']:
T = array([[1,0,0,0],
[0,c,-s,0],
[0,s,c,0],
[0,0,0,0]])
#y-Rotation
if joint_name in ['HeadPitch','LShoulderPitch','RShoulderPitch','LHipPitch','RHipPitch','LKneePitch','LAnklePitch','RKneePitch','RAnklePitch']:
T = array([[c,0,s,0],
[0,1,0,0],
[-s,0,c,0],
[0,0,0,0]])
#z-Rotation
if joint_name in ['HeadYaw','LElbowYaw','RElbowYaw']:
T = array([[c,s,0,0],
[-s,c,0,0],
[0,0,1,0],
[0,0,0,0]])
if joint_name in ['RHipYawPitch','LHipYawPitch']:
y = array([[c,0,s,0],
[0,1,0,0],
[-s,0,c,0],
[0,0,0,0]])
z = array([[c,s,0,0],
[-s,c,0,0],
[0,0,1,0],
[0,0,0,0]])
T = y.dot(z)
#print(joint_name)
#print(T)
T[3][0] = self.lenJoint.get(joint_name)[0]
T[3][1] = self.lenJoint.get(joint_name)[1]
T[3][2] = self.lenJoint.get(joint_name)[2]
T[3][3] = 1
return T
def eval_perceptron(neg_examples, pos_examples, w):
"""
%%
% Evaluates the perceptron using a given weight vector. Here, evaluation
% refers to finding the data points that the perceptron incorrectly classifies.
% Inputs:
% neg_examples - The num_neg_examples x 3 matrix for the examples with target 0.
% num_neg_examples is the number of examples for the negative class.
% pos_examples- The num_pos_examples x 3 matrix for the examples with target 1.
% num_pos_examples is the number of examples for the positive class.
% w - A 3-dimensional weight vector, the last element is the bias.
% Returns:
% mistakes0 - A vector containing the indices of the negative examples that have been
% incorrectly classified as positive.
% mistakes1 - A vector containing the indices of the positive examples that have been
% incorrectly classified as negative.
%%
"""
#num_neg_examples = size(neg_examples,1);
num_neg_examples = neg_examples.shape[0]
#num_pos_examples = size(pos_examples,1);
num_pos_examples = pos_examples.shape[0]
#mistakes0 = [];
mistakes0 = array([], dtype=int)
#mistakes1 = [];
mistakes1 = array([], dtype=int)
#for i=1:num_neg_examples
for i in range(num_neg_examples):
#x = neg_examples(i,:)';
x = neg_examples[i, :]
#activation = x'*w;
#print(x, w)
activation = asscalar(x.dot(w))
#if (activation >= 0)
if activation >= 0:
#mistakes0 = [mistakes0;i];
mistakes0 = r_[mistakes0, i]
#end
#end
#for i=1:num_pos_examples
for i in range(num_pos_examples):
#x = pos_examples(i,:)';
x = pos_examples[i, :]
#activation = x'*w;
#print (w)
activation = asscalar(x.dot(w).astype(float32))
#if (activation < 0)
if activation < 0:
#mistakes1 = [mistakes1;i];
mistakes1 = r_[mistakes1, i]
#end
#end
return mistakes0, mistakes1
def atuliza_matriz_gradientes(self, novos_bias, nova_matriz_greadiente):
if self.gradientes is None:
self.gradientes = nova_matriz_greadiente
self.gradientes_bias = novos_bias
else:
for index_camada, camada in enumerate(self.gradientes):
a = np.array(nova_matriz_greadiente[index_camada]) + np.array(
self.gradientes[index_camada])
self.gradientes[index_camada] = a.tolist()
b = np.array(novos_bias[index_camada]) + np.array(
self.gradientes_bias[index_camada])
self.gradientes_bias[index_camada] = b.tolist()
def calc_rays(xys, view):
"""Return a matrix of column vectors of the rays corresponding to the pixels
xys, given as a list of pairs. view defines the camera. The results are in
camera coordinates."""
something = view_number(view)
cxys = xys - np.array([view.centerx, view.centery])
return np.mat([np.full(len(xys), something), cxys[:, 0], -cxys[:, 1]], dtype=npfloat)
def generateSegmentGraphs(segments, filenames, segmentMarkers, tempos):
# training set timeMarkers = [(26.516,131.312),(4.746,172.450),(41.044,201.012),(82.312,175.997),(15.370,46.003),(122.042,213.469),(30.887,122.294),(0.000,272.304),(37.785,195.357),(15.230,195.357),(37.539,172.498),(67.721,157.716),(37.282,125.899),(147.876,325.127),(14.775,192.008),(213.437,298.800),(29.553,86.022),(238.297,294.371),(21.150,193.356),(41.625,138.350)]
# validation set timeMarkers = [(4.0,141.0),(25.0,177.0),(17.0,188.0),(16.0,129.0),(17.0,177.0),(15.0,136.0),(87.0,149.0),(98.0,173.0),(106.0,212.0),(0.0,104.0)]
timeMarkers = [(37,125)]
myMarkers = [(j.index(min(j.that(selection.start_during_range(i[0], i[0]+1.0)))),j.index(min(j.that(selection.start_during_range(i[1], i[1]+10.0))))) for j,i in zip(segments,timeMarkers)]
for i in range(len(segments)):
pyplot.figure(i,(16,9))
windowLen3 = int((16.0/tempos[i])*60.0/(matlib.mean(matlib.array(segments[i].durations))))
lpf3 = signal.lfilter(np.ones(windowLen3)/windowLen3,1,segments[i].loudness_max) + signal.lfilter(np.ones(windowLen3)/windowLen3,1,segments[i].loudness_max[::-1])[::-1]
lpf3 = np.convolve(segments[i].loudness_max,np.ones(windowLen3)/windowLen3)[windowLen3/2:-(windowLen3/2)]
lpf3[0:windowLen3/2] = lpf3[windowLen3/2]
lpf3[-(windowLen3/2):] = lpf3[-(windowLen3/2)]
pyplot.plot(lpf3)
pyplot.xlabel('Segment Number')
pyplot.ylabel('Loudness (dB)')
#pyplot.vlines(segmentMarkers[i][0], min(lpf3), max(segments[i].loudness_max), 'g')
#pyplot.vlines(segmentMarkers[i][1], min(lpf3), max(segments[i].loudness_max), 'g')
pyplot.vlines(myMarkers[i][0], min(lpf3), max(segments[i].loudness_max), 'r')
pyplot.vlines(myMarkers[i][1], min(lpf3), max(segments[i].loudness_max), 'r')
pyplot.legend(["Loudness", #"Autmatically selected start time: " + str(action.humanize_time(segments[i][segmentMarkers[i][0]].start)),
#"Automatically selected end time: " + str(action.humanize_time(segments[i][segmentMarkers[i][1]].start)),
"Manually selected start time: " + str(action.humanize_time(timeMarkers[i][0])),
"Manually selected end time: " + str(action.humanize_time(timeMarkers[i][1]))])
pyplot.title(filenames[i])
pyplot.show()
def forward_kinematics(self, joints):
'''forward kinematics
:param joints: {joint_name: joint_angle}
'''
for chain_joints in self.chains.values():
T = identity(4)
for joint in chain_joints:
angle = joints[joint]
Tl = self.local_trans(joint, angle)
T = T.dot(Tl)
self.transforms[joint] = T
for i in self.transforms:
if i == 'RElbowRoll':
x = array(self.transforms.get(i))[3][0]
y = array(self.transforms.get(i))[3][1]
z = array(self.transforms.get(i))[3][2]
print "x: " , x , " y: ", y , " z: " , z
def calc_phi(xys, ref_half_plane, view, cameraposor, laserpos, lasertheta):
"""Given an array of pixel pairs xys from a camera with view and cameraposor and
laser with laserpos and lasertheta, calculate the laser inclination based on a
known half-plane ref_half_plane. Throws a NoReferenceException if no pixels are
in the reference half-plane."""
cref_pos = ddd.unrotate(ref_half_plane.pos - cameraposor.pos, cameraposor)
cref_side = ddd.unrotate(ref_half_plane.side, cameraposor)
cref_line = np.cross(cref_side, ddd.unrotate(ref_half_plane.normal, cameraposor), axis = 0)
# TODO less copy-pasta
cpos = np.array([cref_pos[1, 0], -cref_pos[2, 0]]) / cref_pos[0, 0] * ddd.view_number(view) \
+ np.array([view.centerx, view.centery])
cline_ = cref_pos / cref_pos[0, 0] - cref_line / cref_line[0, 0]
cside_ = np.array([cref_side[1, 0], -cref_side[2, 0]])
cside = np.array([cline_[2, 0], cline_[1, 0]])
if np.dot(cside, cside_) < 0:
cside = - cside
dxys = xys - cpos
dot_products = np.array(np.mat([cside]) * np.mat(dxys).T)[0]
good_xys = xys[dot_products >= 0]
print("say "+str(np.average(good_xys[:,1])))
if len(good_xys) == 0:
raise NoReferenceException()
threepoints = ddd.threedize_plane(good_xys, view, cameraposor, ref_half_plane)
return calc_phi_points(threepoints, laserpos, lasertheta)
def Intersec(s, o, v):
cent = np.array(s[0:3])
rad = s[3]
vt = o - cent
a = np.dot(v, v)
b = 2 * np.dot(v, vt)
c = np.dot(vt, vt) - rad * rad
sol, t1, t2 = SolveTri(a, b, c)
if sol == 2:
if t1 < t2:
t = t1
else:
t = t2
elif sol == 1:
t = t1
else:
t = HUGE_VAL
return t
def Intersec(s,o,v): cent=np.array(s[0:3]) rad=s[3] vt=o-cent a=np.dot(v,v) b=2*np.dot(v,vt) c=np.dot(vt,vt)-rad*rad sol,t1,t2=SolveTri(a,b,c) if sol==2: if t1<t2: t=t1 else: t=t2 elif sol==1: t=t1 else: t=HUGE_VAL return t
h = screen['h']
ratiox = screen['ratiox']
ratioy = screen['ratioy']
if len(sys.argv) > 2:
w = int(sys.argv[2])
if len(sys.argv) > 3:
h = int(sys.argv[3])
if len(sys.argv) > 4:
fnameout = sys.argv[4]
if len(sys.argv) > 5:
ratiox = float(sys.argv[5])
if len(sys.argv) > 6:
ratioy = float(sys.argv[6])
ww = 1. * ratiox
hh = ww * h / w * ratioy
e = np.array(camera['loc'], dtype=float)
f = np.array(camera['front'], dtype=float)
u = np.array(camera['up'], dtype=float)
sphs = []
for sph in objects:
sphs += [np.array(sph['data'], dtype=float)]
u /= np.linalg.norm(u)
r = np.cross(f, u)
u = np.cross(r, f)
u /= np.linalg.norm(u)
r /= np.linalg.norm(r)
def SolveTri(a, b, c):
d = b * b - 4 * a * c
t1, t2 = 0., 0.
h=screen['h'] ratiox=screen['ratiox'] ratioy=screen['ratioy'] if len(sys.argv) > 2: w=int(sys.argv[2]) if len(sys.argv) > 3: h=int(sys.argv[3]) if len(sys.argv) > 4: fnameout=sys.argv[4] if len(sys.argv) > 5: ratiox=float(sys.argv[5]) if len(sys.argv) > 6: ratioy=float(sys.argv[6]) ww=1.*ratiox hh=ww*h/w*ratioy e=np.array(camera['loc'],dtype=float) f=np.array(camera['front'],dtype=float) u=np.array(camera['up'],dtype=float) sphs=[] for sph in objects: sphs += [np.array(sph['data'],dtype=float)] u/=np.linalg.norm(u) r=np.cross(f,u) u=np.cross(r,f) u/=np.linalg.norm(u) r/=np.linalg.norm(r) def SolveTri(a,b,c): d=b*b-4*a*c t1,t2 = 0., 0. sol=0
# coding:utf-8 from matplotlib.pyplot import plot, show, figure from numpy.matlib import randn, array, vstack, where from scipy.cluster.vq import kmeans, vq # 生成正态分布的二维数据 class1 = 1.5 * randn(100, 2) class2 = randn(100, 2) + array([5, 5]) features = vstack((class1, class2)) # 用k=2对这些数据进行聚类 centroids, variance = kmeans(features, 2) # 通过矢量化函数对每个数据点进行归类: code, distance = vq(features, centroids) figure() ndx = where(code == 0)[0] plot(features[ndx, 0], features[ndx, 1], '*') ndx = where(code == 1)[0] plot(features[ndx, 0], features[ndx, 1], 'r.') plot(features[:, 0], features[:, 1], 'go') show()
ww = 1. * ratiox
hh = ww * h / w * ratioy
if len(sys.argv) > 1:
w = int(sys.argv[1])
if len(sys.argv) > 2:
h = int(sys.argv[2])
if len(sys.argv) > 3:
fnameout = sys.argv[3]
if len(sys.argv) > 4:
ratiox = float(sys.argv[4])
if len(sys.argv) > 5:
ratioy = float(sys.argv[5])
sphs = []
sphs += [np.array([0, -0.1, 0, 0.05, 0.8, 0.8, 0.8], dtype=float)]
sphs += [np.array([0, 0, 0, 0.05, 0.8, 0.8, 0.8], dtype=float)]
sphs += [np.array([0, 0.1, 0, 0.05, 0.8, 0.8, 0.8], dtype=float)]
sphs += [np.array([0.1, -0.05, 0, 0.05, 0.8, 0, 0], dtype=float)]
sphs += [np.array([0.1, 0.05, 0, 0.05, 0, 0, 0.8], dtype=float)]
sphs += [np.array([0.2, 0, 0, 0.05, 0, 0.8, 0], dtype=float)]
sphs += [np.array([0.05, -0.05, 0.1, 0.05, 0.8, 0.8, 0.8], dtype=float)]
sphs += [np.array([0.05, 0.05, 0.1, 0.05, 0.8, 0.8, 0.8], dtype=float)]
sphs += [np.array([0, -0.5, 0.5, 0.02, 1, 1, 0], dtype=float)]
e = np.array([0.4, 0, 0.4], dtype=float)
f = np.array([-1, 0, -1], dtype=float)
u = np.array([-0.707107, 0, 0.707107], dtype=float)
u /= np.linalg.norm(u)
r = np.cross(f, u)
u = np.cross(r, f)
def softmax(self, y):
maxy = np.amax(y)
probas = np.exp(y-maxy)
probas = probas / probas.sum(axis=0)
return np.array(probas)
