def __call__(self):
"Return the locations of the ticks"
b = self._transform.base
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = self._transform.transform_point((vmin, vmax))
if vmax < vmin:
vmin, vmax = vmax, vmin
numdec = math.floor(vmax) - math.ceil(vmin)
if self._subs is None:
if numdec > 10:
subs = np.array([1.0])
elif numdec > 6:
subs = np.arange(2.0, b, 2.0)
else:
subs = np.arange(2.0, b)
else:
subs = np.asarray(self._subs)
stride = 1
while numdec / stride + 1 > self.numticks:
stride += 1
decades = np.arange(math.floor(vmin), math.ceil(vmax) + stride, stride)
if len(subs) > 1 or subs[0] != 1.0:
ticklocs = []
for decade in decades:
ticklocs.extend(subs * (np.sign(decade) * b ** np.abs(decade)))
else:
ticklocs = np.sign(decades) * b ** np.abs(decades)
return np.array(ticklocs)
def FWHM(X,Y):
half_max = (max(Y)+min(Y)) / 2
d = np.sign(half_max - np.array(Y[0:-1])) - np.sign(half_max - np.array(Y[1:]))
#find the left and right most indexes
left_idx = find(d > 0)[0]
right_idx = find(d < 0)[-1]
return X[right_idx], X[left_idx], half_max #return the difference (full width)
def __bisect(f, a, b, args):
tol = 4.4408920985006262e-16
fa = f(*(a,)+args)
fb = f(*(b,)+args)
if np.sign(fa) == np.sign(fb):
raise RuntimeError('f(a) and f(b) must have different signs')
while abs(fa) > tol and abs(fb) > tol:
ab = (a + b) / 2.
fab = f(*(ab,)+args)
if cmp(fa, 0) != cmp(fab, 0):
b = ab
fb = fab
elif cmp(fb, 0) != cmp(fab, 0):
a = ab
fa = fab
else:
raise RuntimeError('Something fishy happened during bisection')
if abs(fa) < tol and abs(fb) < tol:
return a if min(abs(fa), abs(fb)) == abs(fa) else b
elif abs(fa) < tol:
return a
elif abs(fb) < tol:
return b
else:
raise RuntimeError('Something fishy happened during bisection')
def get_N_mtx(self,r_pnt, node_ls_values, r_ls_value):
'''
Returns the matrix of the shape functions used for the field approximation
containing zero entries. The number of rows corresponds to the number of nodal
dofs. The matrix is evaluated for the specified local coordinate r.
'''
p_N_mtx = self.parent_fets.get_N_mtx(r_pnt)
n_nodes = self.parent_fets.n_e_dofs/self.parent_fets.n_nodal_dofs
p_nodal_dofs = self.parent_fets.n_e_dofs/self.parent_fets.n_nodal_dofs
p_value = sign(r_ls_value)
N_e_list = [p_N_mtx[:,i*4:i*4+2]* \
(p_value-sign(node_ls_values[i]))\
for i in range(0,n_nodes)]
N_e_mtx = hstack(N_e_list)
N_enr_mtx = zeros((2,self.parent_fets.n_e_dofs*6), dtype = 'float_')
for i in range(0,p_nodal_dofs):
N_enr_mtx[0,i] = p_N_mtx[0,i]#s
N_enr_mtx[1,i] = p_N_mtx[0,i]
N_enr_mtx[0,i+2] = p_N_mtx[0,i]#m
N_enr_mtx[1,i+4] = p_N_mtx[0,i]#f
N_enr_mtx[0,i+6] = N_e_mtx[0,i]#sx
N_enr_mtx[1,i+6] = N_e_mtx[0,i]#sx
N_enr_mtx[0,i+8] = N_e_mtx[0,i]#mx
N_enr_mtx[1,i+10] = N_e_mtx[0,i]#fx
return N_enr_mtx
def get_ry0_distance(self, mesh):
"""
:param mesh:
:class:`~openquake.hazardlib.geo.mesh.Mesh` of points to calculate
Ry0-distance to.
:returns:
Numpy array of distances in km.
See also :meth:`superclass method <.base.BaseSurface.get_ry0_distance>`
for spec of input and result values.
This is version specific to the planar surface doesn't make use of the
mesh
"""
dst1 = geodetic.distance_to_arc(self.top_left.longitude,
self.top_left.latitude,
(self.strike + 90.) % 360,
mesh.lons, mesh.lats)
dst2 = geodetic.distance_to_arc(self.top_right.longitude,
self.top_right.latitude,
(self.strike + 90.) % 360,
mesh.lons, mesh.lats)
# Find the points on the rupture
# Get the shortest distance from the two lines
idx = numpy.sign(dst1) == numpy.sign(dst2)
dst = numpy.zeros_like(dst1)
dst[idx] = numpy.fmin(numpy.abs(dst1[idx]), numpy.abs(dst2[idx]))
return dst
def addFace(self, du, dv, d, ru=0.5, rv=0.5):
""" Creates a set of rectangular surfaces, their IDs, and face dims.
nu,nv: number of surfaces in the u and v directions
du,dv: {1,2,3} maps to {x,y,z}; negative sign means reverse order
d: position of the surfaces in the remaining coordinate axis
ru,rv: surfaces span -ru to +ru in u dir. and -rv to +rv in v dir.
Adds to self.Ps and self.Ks
"""
self.faces.append([du,dv])
nP = 10
ni = self.ms[abs(du)-1].shape[0]
nj = self.ms[abs(dv)-1].shape[0]
verts = numpy.zeros((2,2,3),order='F')
verts[:,:,:] = d
verts[0,:,abs(du)-1] = -ru*numpy.sign(du)
verts[1,:,abs(du)-1] = ru*numpy.sign(du)
verts[:,0,abs(dv)-1] = -rv*numpy.sign(dv)
verts[:,1,abs(dv)-1] = rv*numpy.sign(dv)
for j in range(nj):
for i in range(ni):
self.Ps.append(PGMlib.bilinearinterp(nP, ni, nj, i+1, j+1, verts))
if len(self.Ks) > 0:
counter = numpy.max(self.Ks[-1]) + 1
else:
counter = 0
K = numpy.zeros((ni,nj),int)
for j in range(nj):
for i in range(ni):
K[i,j] = counter
counter += 1
self.Ks.append(K)
self.outers.append(numpy.ones((ni,nj),bool))
def forward(self, state, action, Reward, state_dash, episode_end):
num_of_batch = state.shape[0]
s = Variable(state)
s_dash = Variable(state_dash)
Q = self.Q_func(s) # Get Q-value
# Generate Target Signals
tmp2 = self.Q_func(s_dash)
tmp2 = list(map(np.argmax, tmp2.data.get())) # argmaxQ(s',a)
tmp = self.Q_func_target(s_dash) # Q'(s',*)
tmp = list(tmp.data.get())
# select Q'(s',*) due to argmaxQ(s',a)
res1 = []
for i in range(num_of_batch):
res1.append(tmp[i][tmp2[i]])
#max_Q_dash = np.asanyarray(tmp, dtype=np.float32)
max_Q_dash = np.asanyarray(res1, dtype=np.float32)
target = np.asanyarray(Q.data.get(), dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = np.sign(Reward[i]) + self.gamma * max_Q_dash[i]
else:
tmp_ = np.sign(Reward[i])
action_index = self.action_to_index(action[i])
target[i, action_index] = tmp_
# TD-error clipping
td = Variable(cuda.to_gpu(target)) - Q # TD error
td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division
td_clip = td * (abs(td.data) <= 1) + td/abs(td_tmp) * (abs(td.data) > 1)
zero_val = Variable(cuda.to_gpu(np.zeros((self.replay_size, self.num_of_actions), dtype=np.float32)))
loss = F.mean_squared_error(td_clip, zero_val)
return loss, Q
def _sym_ortho(a, b):
"""
Stable implementation of Givens rotation.
Notes
-----
The routine 'SymOrtho' was added for numerical stability. This is
recommended by S.-C. Choi in [1]_. It removes the unpleasant potential of
``1/eps`` in some important places (see, for example text following
"Compute the next plane rotation Qk" in minres.py).
References
----------
.. [1] S.-C. Choi, "Iterative Methods for Singular Linear Equations
and Least-Squares Problems", Dissertation,
http://www.stanford.edu/group/SOL/dissertations/sou-cheng-choi-thesis.pdf
"""
if b == 0:
return np.sign(a), 0, abs(a)
elif a == 0:
return 0, np.sign(b), abs(b)
elif abs(b) > abs(a):
tau = a / b
s = np.sign(b) / sqrt(1 + tau * tau)
c = s * tau
r = b / s
else:
tau = b / a
c = np.sign(a) / sqrt(1+tau*tau)
s = c * tau
r = a / c
return c, s, r
def start(self, f, a, b, args=()):
r"""Prepare for the iterations."""
self.function_calls = 0
self.iterations = 0
self.f = f
self.args = args
self.ab[:] = [a, b]
if not np.isfinite(a) or np.imag(a) != 0:
raise ValueError("Invalid x value: %s " % (a))
if not np.isfinite(b) or np.imag(b) != 0:
raise ValueError("Invalid x value: %s " % (b))
fa = self._callf(a)
if not np.isfinite(fa) or np.imag(fa) != 0:
raise ValueError("Invalid function value: f(%f) -> %s " % (a, fa))
if fa == 0:
return _ECONVERGED, a
fb = self._callf(b)
if not np.isfinite(fb) or np.imag(fb) != 0:
raise ValueError("Invalid function value: f(%f) -> %s " % (b, fb))
if fb == 0:
return _ECONVERGED, b
if np.sign(fb) * np.sign(fa) > 0:
raise ValueError("a, b must bracket a root f(%e)=%e, f(%e)=%e " %
(a, fa, b, fb))
self.fab[:] = [fa, fb]
return _EINPROGRESS, sum(self.ab) / 2.0
def _newton_quadratic(ab, fab, d, fd, k):
"""Apply Newton-Raphson like steps, using divided differences to approximate f'
ab is a real interval [a, b] containing a root,
fab holds the real values of f(a), f(b)
d is a real number outside [ab, b]
k is the number of steps to apply
"""
a, b = ab
fa, fb = fab
_, B, A = _compute_divided_differences([a, b, d], [fa, fb, fd],
forward=True, full=False)
# _P is the quadratic polynomial through the 3 points
def _P(x):
# Horner evaluation of fa + B * (x - a) + A * (x - a) * (x - b)
return (A * (x - b) + B) * (x - a) + fa
if A == 0:
r = a - fa / B
else:
r = (a if np.sign(A) * np.sign(fa) > 0 else b)
# Apply k Newton-Raphson steps to _P(x), starting from x=r
for i in range(k):
r1 = r - _P(r) / (B + A * (2 * r - a - b))
if not (ab[0] < r1 < ab[1]):
if (ab[0] < r < ab[1]):
return r
r = sum(ab) / 2.0
break
r = r1
return r
def increase(self, qty, px, fees=0, **kwargs):
if not self.is_open():
raise Exception('increase position failed: no position currently open')
if np.sign(self._live_qty) != np.sign(qty):
msg = 'increase position failed: trade quantity {0} is different sign as live quantity {1}'
raise Exception(msg.format(qty, self._live_qty))
self._order(qty, px, fees, **kwargs)
def adaboost_clf(Y_train, X_train, Y_test, X_test, M, clf):
n_train, n_test = len(X_train), len(X_test)
# Initialize weights
w = np.ones(n_train) / n_train
pred_train, pred_test = [np.zeros(n_train), np.zeros(n_test)]
for i in range(M):
# Fit a classifier with the specific weights
clf.fit(X_train, Y_train, sample_weight = w)
pred_train_i = clf.predict(X_train)
pred_test_i = clf.predict(X_test)
# Indicator function
miss = [int(x) for x in (pred_train_i != Y_train)]
# Equivalent with 1/-1 to update weights
miss2 = [x if x==1 else -1 for x in miss]
# Error
err_m = np.dot(w,miss) / sum(w)
# Alpha
alpha_m = 0.5 * np.log( (1 - err_m) / float(err_m))
# New weights
w = np.multiply(w, np.exp([float(x) * alpha_m for x in miss2]))
# Add to prediction
pred_train = [sum(x) for x in zip(pred_train,
[x * alpha_m for x in pred_train_i])]
pred_test = [sum(x) for x in zip(pred_test,
[x * alpha_m for x in pred_test_i])]
pred_train, pred_test = np.sign(pred_train), np.sign(pred_test)
# Return error rate in train and test set
return get_error_rate(pred_train, Y_train), \
get_error_rate(pred_test, Y_test)
def slow_set_freq(self,freq,step):
current_f=float(self.get_freq())/1000
print(current_f)
detun_f=freq-current_f
ns=np.floor(detun_f/step)
print('ns',ns)
f_set=current_f
for i in range(int(abs(ns))):
f_set=f_set+np.sign(detun_f)*step
print(f_set)
self.set_freq(f_set)
print('Start moving cavity slowly...')
current_f=float(self.get_freq())/1000
detun_f=freq-current_f
f_set=current_f
print('fine moves')
for i in range(int(abs(detun_f))):
f_set=f_set+np.sign(detun_f)
print(f_set)
self.set_freq(f_set)
print('Finish moving')
print('Final freq',self.get_freq())
def turnStep(self, deg, blocking, anly):
self.setStep(deg)
if blocking: # If nudge
steps = deg / (eleMaxStep / self.microsteps)
self.wait = diode_wait / self.microsteps
# If nudging would put elevation too high or low
if -steps + self.cnt > ele_steps: # If nudging would put elevation too high
steps = -ele_steps + self.cnt
elif -steps + self.cnt < 0: # If nudging would put elevation too low
steps = self.cnt
else:
if anly:
steps = (np.sign(deg) * self.microsteps * deg_tol / eleMaxStep) / 2
else:
steps = np.sign(deg) * self.microsteps * deg_tol / eleMaxStep
if steps:
if anly:
self.wait = (diode_wait / abs(steps)) / 4
else:
self.wait = (diode_wait / abs(steps))
if abs(deg) >= deg_tol:
if self.cnt >= 0:
if self.cnt < ele_steps:
self.move(steps)
elif self.cnt >= ele_steps and steps > 0:
self.move(steps)
else:
time.sleep(diode_wait / 2)
elif self.cnt < 0 and steps < 0:
self.move(steps)
else:
time.sleep(diode_wait / 2)
else:
time.sleep(diode_wait / 2)
def waypoints_to_commands(coords): #cmd = [[vx,az,time],etc] #Convert waypoints to value in stage lin_vel = 0.2 ang_vel = math.radians(45) #45 deg/s in rad/s init_ang = 0; move_ang = [0] move_dist = [0] for i in range(len(coords)-1): p1 = coords[i] p2 = coords[i+1] move_ang.append(math.atan2(p2[1]-p1[1],p2[0]-p1[0])) move_dist.append(math.sqrt((p2[1]-p1[1])**2+(p2[0]-p1[0])**2)) print np.degrees(move_ang) print len(move_dist) move_cmd = [] for i in range(len(move_ang)-1): ang_cmd = (move_ang[i+1]-move_ang[i]) ang_time = ang_cmd/ang_vel dist_cmd =move_dist[i+1]-move_dist[i] dist_time = dist_cmd/lin_vel move_cmd.append([0,np.sign(ang_cmd),math.fabs(ang_time)]) move_cmd.append([np.sign(dist_cmd),0,math.fabs(dist_time)]) print move_cmd print len(move_cmd) return move_cmd
def prob_flat(m1, m2, s1z, s2z, **kwargs):
''' Return probability density for uniform in component mass
Parameters
----------
m1: array
Component masses 1
m2: array
Component masses 2
s1z: array
Aligned spin 1 (not in use currently)
s2z:
Aligned spin 2 (not in use currently)
**kwargs: string
Keyword arguments as model parameters
Returns
-------
p_m1_m2: array
the probability density for m1, m2 pair
'''
min_mass = kwargs.get('min_mass', 1.)
max_mass = kwargs.get('max_mass', 2.)
bound = np.sign(m1 - m2)
bound += np.sign(max_mass - m1) * np.sign(m2 - min_mass)
idx = np.where(bound != 2)
p_m1_m2 = 2. / (max_mass - min_mass)**2
p_m1_m2[idx] = 0
return p_m1_m2
def example_classification(method='pa', classification_loss='hingeloss', initial_learning_rate=1e-4,
tol=0.01):
noise = 0.1
rng = np.random.RandomState(0)
U_true = rng.randn(50, 2)
V_true = rng.randn(40, 2)
X = np.sign(np.dot(U_true, V_true.T) + noise * rng.randn(50, 40))
print(X[:5, :5])
# Put aside 1500 entries for testing
row_mask = rng.randint(0, 50, size=1500)
col_mask = rng.randint(0, 40, size=1500)
# Get a mask for the remaining (training) observations
mask = np.zeros((50, 40), dtype=np.bool)
mask[row_mask, col_mask] = 1
fit_mask = (~mask).nonzero()
MF = MatrixCompletion(method=method, is_classification=True,
classification_loss = classification_loss,
initial_learning_rate=initial_learning_rate,
n_components=2,random_state=0, alpha=0.1,
verbose=10, shuffle=True, tol=tol)
MF.fit(X, mask=fit_mask)
U, V = MF.U_, MF.V_
X_pred = np.dot(U, V.T)
test_acc = np.mean(np.sign(X_pred[row_mask, col_mask]) ==
X[row_mask, col_mask])
print("Test accuracy:", test_acc)
print(np.dot(U, V.T)[:5, :5])
def _prepare_input_epr_mur(w,epb,mub,ange,angm,sigmadc,tau):
epr = _np.zeros((len(epb),len(w)),dtype=complex)
mur = _np.zeros((len(epb),len(w)),dtype=complex)
for j in range(len(epb)):
epr[j,:] = epb[j]*(1-1j*_np.sign(w)*_np.tan(ange[j])) + sigmadc[j]/(1+1j*w*tau[j])/(1j*w*_ep0)
mur[j,:] = mub[j]*(1-1j*_np.sign(w)*_np.tan(angm[j]))
return epr, mur
def __init__(self, direction, hp, **kwargs):
"""Initialize a Critter
`direction`: direction in radians that the critter is facing initially
`hp`: Health of the Critter
"""
Widget.__init__(self, **kwargs)
# Max health
self.hp = hp
# Direction we're facing currently
self.direction = direction
# Speed in tiles per second
self.speed = CRITTER_SPEED
# Damage done (accessed through .damage)
self._damage = 0
# Are we dead yet?
self.dead = False
# x-component of velocity
self.xdir = int(numpy.sign(round(numpy.cos(direction))))
# y-component of velocity
self.ydir = int(numpy.sign(round(numpy.sin(direction))))
# Initial velocity
self.initial_dir = self.xdir, self.ydir
self.draw()
Clock.schedule_once(self.go)
Clock.schedule_once(self.tick)
def convertDec(deg, min, sec):
if np.size(deg) == 1:
sign = +1 if deg==0 else np.sign(deg)
else:
sign = np.sign(deg)
sign[deg==0] = +1
return sign * (np.abs(deg) + (min + sec/60.)/60.)
def find_correct_sign(z1,z2,z_approx):
'''
Create new vector from z1, z2 choosing elements with sign matching z_approx
This is used when you have to make a root choice on a complex number.
and you know the approximate value of the root.
.. math::
z1,z2 = \\pm \\sqrt(z^2)
Parameters
------------
z1 : array-like
root 1
z2 : array-like
root 2
z_approx : array-like
approximate answer of z
Returns
----------
z3 : npy.array
array built from z1 and z2 by
z1 where sign(z1) == sign(z_approx), z2 else
'''
return npy.where(
npy.sign(npy.angle(z1)) == npy.sign(npy.angle(z_approx)),z1, z2)
def deflections(self, xin, yin):
import numpy
from math import cos, sin, pi
from scipy.special import gammainc
x, y = self.align_coords(xin, yin)
q = self.q
if q == 1.:
q = 1. - 1e-7 # Avoid divide-by-zero errors
b, n, re = self.b, self.n, self.re
k = 2. * n - 1. / 3 + 4. / (405. * n) + 46 / (25515. * n**2)
amp = (b / re)**2 / gammainc(2 * n, k * (b / re)**(1 / n))
r0 = re / q**0.5
o = numpy.ones(x.size).astype(x.dtype)
eval = numpy.array(
[abs(x).ravel() / r0,
abs(y).ravel() / r0, n * o, q * o]).T
xout = numpy.sign(x) * (amp * self.xmod.eval(eval) * r0).reshape(
x.shape)
yout = numpy.sign(y) * (amp * self.ymod.eval(eval) * r0).reshape(
y.shape)
theta = -(self.theta - pi / 2.)
ctheta = cos(theta)
stheta = sin(theta)
x = xout * ctheta + yout * stheta
y = yout * ctheta - xout * stheta
return x, y
def get_corr_pred(self, eps, d_eps, sig, t_n, t_n1, alpha, q, kappa):
# g = lambda k: 0.8 - 0.8 * np.exp(-k)
# g = lambda k: 1. / (1 + np.exp(-2 * k + 6.))
n_e, n_ip, n_s = eps.shape
D = np.zeros((n_e, n_ip, 3, 3))
D[:, :, 0, 0] = self.E_m
D[:, :, 2, 2] = self.E_f
sig_trial = sig[:, :, 1]/(1-self.g(kappa)) + self.E_b * d_eps[:,:, 1]
xi_trial = sig_trial - q
f_trial = abs(xi_trial) - (self.sigma_y + self.K_bar * alpha)
elas = f_trial <= 1e-8
plas = f_trial > 1e-8
d_sig = np.einsum('...st,...t->...s', D, d_eps)
sig += d_sig
d_gamma = f_trial / (self.E_b + self.K_bar + self.H_bar) * plas
alpha += d_gamma
kappa += d_gamma
q += d_gamma * self.H_bar * np.sign(xi_trial)
w = self.g(kappa)
sig_e = sig_trial - d_gamma * self.E_b * np.sign(xi_trial)
sig[:, :, 1] = (1-w)*sig_e
E_p = -self.E_b / (self.E_b + self.K_bar + self.H_bar) * derivative(self.g, kappa, dx=1e-6) * sig_e \
+ (1 - w) * self.E_b * (self.K_bar + self.H_bar) / \
(self.E_b + self.K_bar + self.H_bar)
D[:, :, 1, 1] = (1-w)*self.E_b*elas + E_p*plas
return sig, D, alpha, q, kappa
def main():
file1 = sys.argv[1]
data = numpy.loadtxt(file1)
train = data[25:]
valid = data[0:25]
train_newx = transform(train[:,0:2])
val_newx = transform(valid[:,0:2])
file2 = sys.argv[2]
out = numpy.loadtxt(file2)
out_newx = transform(out[:,0:2])
for i in range(0, train_newx.shape[1]):
weights, E_in = linreg(train_newx[:,0:i+1], train[:,2])
val_y = numpy.sign(numpy.dot(val_newx[:,0:i+1], weights))
val_error = sum(val_y != valid[:,2])/float(len(valid))
print 'Validation error for k =', i, ':', val_error
out_y = numpy.sign(numpy.dot(out_newx[:,0:i+1], weights))
out_error = sum(out_y != out[:,2])/float(len(out))
print 'Out of sample error for k =', i, ':', out_error
def findspikes(t,x,dxdt):
"""Searching for spikes in a TODL LTC2442 data series. The algorithm
searches for the given threshold. If it is found and the subsequent
data exceeds the threshold as well with a negative sign its defined as
a spike
Args:
t: time
x: data
dxdt: Threshold for rejection, a working rejection for FP07 is 0.1 [V/s]
"""
#print('Despiking')
dt = np.diff(t)
dx = np.diff(x)
spikes = np.zeros(np.shape(t))
for i in range(1,len(dt)-1):
dxdt1 = dx[i]/dt[i]
dxdt2 = dx[i+1]/dt[i+1]
if(abs(dxdt1) > dxdt):
if(abs(dxdt1) > dxdt):
if(np.sign(dxdt1) == -np.sign(dxdt2)):
spikes[i+1] = 1
#print('Done despiking')
return spikes
def run( self, X, y, callback):
# through the epochs
for e in xrange(self.epochs):
# compute gradients
self.compute_dEdW(X, y, self.weights)
# through the layers
for i in xrange(self.nn_size-1):
# select weights
sel_pos = (self.dEdW_pre[i] * self.dEdW[i])>0.0
sel_neg = (self.dEdW_pre[i] * self.dEdW[i])<0.0
sel_equ = (self.dEdW_pre[i] * self.dEdW[i])==0.0
# if dEdW[i]*dEdW_pre[i] > 0
self.Delta[i][sel_pos] = np.minimum(self.Delta_pre[i][sel_pos]*self.eta_plus, self.Delta_max)
self.Delta_w[i][sel_pos] = -np.sign(self.dEdW[i][sel_pos]) * self.Delta[i][sel_pos]
self.W[i][sel_pos] = self.W[i][sel_pos] + self.Delta_w[i][sel_pos]
# if dEdW[i]*dEdW_pre[i] < 0
self.Delta[i][sel_neg] = np.maximum(self.Delta_pre[i][sel_neg]*self.eta_minus, self.Delta_min)
if self.E>self.E_pre:
self.W[i][sel_neg] = self.W[i][sel_neg] - self.Delta_w_pre[i][sel_neg]
self.dEdW[i][sel_neg] = 0.0
# if dEdW[i]*dEdW_pre[i] == 0
self.Delta_w[i][sel_equ] = -np.sign(self.dEdW[i][sel_equ]) * self.Delta[i][sel_equ]
self.W[i][sel_equ] = self.W[i][sel_equ] + self.Delta_w[i][sel_equ]
# update
self.dEdW_pre[i] = self.dEdW[i]
self.Delta_pre[i] = self.Delta[i]
self.Delta_w_pre[i] = self.Delta_w[i]
self.E_pre = self.E
self.E = callback(e)
def testDigits(kTup=('rbf', 10)):
data, labels = loadImages('trainingDigits')
b, alphas = smo(data, labels, 200, 0.0001, 10000, kTup)
dataMat = np.mat(data)
labelMat = np.mat(labels).transpose()
svInd = np.nonzero(alphas.A > 0)[0]
sVs = dataMat[svInd]
labelSV = labelMat[svInd]
print "There are %d Support Vectors" % np.shape(sVs)[0]
m, n = np.shape(dataMat)
errorCount = 0
for i in xrange(m):
kernelEval = kernelTransform(sVs, dataMat[i, :], kTup)
predict = kernelEval.T * np.multiply(labelSV, alphas[svInd]) + b
if np.sign(predict) != np.sign(labels[i]):
errorCount += 1
print "The training error rate is %f " % (float(errorCount) / m)
data, labels = loadImages('testDigits')
dataMat = np.mat(data)
labelMat = np.mat(labels).transpose()
m, n = np.shape(dataMat)
errorCount = 0
for i in xrange(m):
kernelEval = kernelTransform(sVs, dataMat[i, :], kTup)
predict = kernelEval.T * np.multiply(labelSV, alphas[svInd]) + b
if np.sign(predict) != np.sign(labels[i]):
errorCount += 1
print "The test error rate is %f " % (float(errorCount) / m)
def get_loss(self, state, action, reward, state_prime, episode_end):
s = Variable(cuda.to_gpu(state))
s_dash = Variable(cuda.to_gpu(state_prime))
q = self.model.q_function(s) # Get Q-value
# Generate Target Signals
tmp = self.model_target.q_function(s_dash) # Q(s',*)
tmp = list(map(np.max, tmp.data)) # max_a Q(s',a)
max_q_prime = np.asanyarray(tmp, dtype=np.float32)
target = np.asanyarray(copy.deepcopy(q.data.get()), dtype=np.float32)
for i in range(self.replay_size):
if episode_end[i][0] is True:
tmp_ = np.sign(reward[i])
else:
# The sign of reward is used as the reward of DQN!
tmp_ = np.sign(reward[i]) + self.gamma * max_q_prime[i]
target[i, action[i]] = tmp_
# TD-error clipping
td = Variable(cuda.to_gpu(target)) - q # TD error
td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division
td_clip = td * (abs(td.data) <= 1) + td/abs(td_tmp) * (abs(td.data) > 1)
zero_val = Variable(cuda.to_gpu(np.zeros((self.replay_size, self.n_act), dtype=np.float32)))
loss = F.mean_squared_error(td_clip, zero_val)
return loss, q
def __init__( self, sides, size, color, position = (0.0, 0.0, 0.0) ):
self.sides = sides
self.color = color
self.size = size
self.position = np.array(position)
height = LEGO_BIG_HEIGHT if self.size else LEGO_SMALL_HEIGHT
length = self.bottom - self.top
combine = lambda _points, _vertices, _weights: _points
self.__tess = GLU.gluNewTess()
GLU.gluTessCallback(self.__tess, GLU.GLU_TESS_BEGIN, GL.glBegin)
GLU.gluTessCallback(self.__tess,GLU.GLU_TESS_VERTEX,GL.glVertex3fv)
GLU.gluTessCallback(self.__tess,GLU.GLU_TESS_COMBINE,combine)
GLU.gluTessCallback(self.__tess, GLU.GLU_TESS_END, GL.glEnd)
GLU.gluTessProperty(self.__tess, GLU.GLU_TESS_WINDING_RULE, GLU.GLU_TESS_WINDING_ODD)
self.__gllist = GL.glGenLists(1)
GL.glNewList(self.__gllist,GL.GL_COMPILE)
GL.glColor3fv(self.color)
GL.glBindTexture(GL.GL_TEXTURE_2D,0)
GLU.gluTessNormal(self.__tess, 0.0, 1.0, 0.0)
GL.glNormal3f(0.0, 1.0, 0.0)
GLU.gluTessBeginPolygon(self.__tess,None)
GLU.gluTessBeginContour(self.__tess)
for i in range(0,len(self.coords)):
vertex = (self.coords[i][0]*LEGO_GRID, height, self.coords[i-1][1]*LEGO_GRID)
GLU.gluTessVertex(self.__tess, vertex, vertex)
vertex = (self.coords[i][0]*LEGO_GRID, height, self.coords[i][1]*LEGO_GRID)
GLU.gluTessVertex(self.__tess, vertex, vertex)
GLU.gluTessEndContour(self.__tess)
GLU.gluTessEndPolygon(self.__tess)
for i in range(0,len(self.coords)):
GL.glBegin(GL.GL_QUADS)
sign = float(np.sign(self.sides[2*i-1]))
GL.glNormal3f( sign, 0.0, 0.0 )
GL.glVertex3f( self.coords[i][0] * LEGO_GRID, height, self.coords[i-1][1] * LEGO_GRID)
GL.glVertex3f( self.coords[i][0] * LEGO_GRID, height, self.coords[i] [1] * LEGO_GRID)
GL.glVertex3f( self.coords[i][0] * LEGO_GRID, 0.0, self.coords[i] [1] * LEGO_GRID)
GL.glVertex3f( self.coords[i][0] * LEGO_GRID, 0.0, self.coords[i-1][1] * LEGO_GRID)
sign = float(np.sign(self.sides[2*i-2]))
GL.glNormal3f( 0.0, 0.0, -sign )
GL.glVertex3f( self.coords[i-1][0] * LEGO_GRID, height, self.coords[i-1][1] * LEGO_GRID )
GL.glVertex3f( self.coords[i] [0] * LEGO_GRID, height, self.coords[i-1][1] * LEGO_GRID )
GL.glVertex3f( self.coords[i] [0] * LEGO_GRID, 0.0, self.coords[i-1][1] * LEGO_GRID )
GL.glVertex3f( self.coords[i-1][0] * LEGO_GRID, 0.0, self.coords[i-1][1] * LEGO_GRID )
GL.glEnd()
GL.glTranslatef( self.left*LEGO_GRID + LEGO_GRID/2.0, (LEGO_BUMP_HEIGHT+height)/2.0 , self.bottom*LEGO_GRID - LEGO_GRID/2.0 )
for i in range( self.left, self.right ):
for j in range( self.bottom, self.top ):
GL.glTranslatef( 0.0, 0.0, LEGO_GRID )
if self.is_hit( (i+0.5,j+0.5) ):
_caped_cylinder( LEGO_BUMP_RADIUS, height+LEGO_BUMP_HEIGHT, 32 )
GL.glTranslatef( 0.0, 0.0, length*LEGO_GRID )
GL.glTranslatef( LEGO_GRID, 0.0, 0.0 )
GL.glEndList()
def intercept_poly(self, r, p, k):
S = r.__class__
u = self.curvature*np.sign(self.offset[2])
if u == 0.:
r, f, fr, g = Element.intercept_poly(self, r, p, k)
else:
p1 = p.copy().shift(1)
a = (-u*k).shift(1)
a -= (a*a - p1*r*u**2)**.5
a = a*p1**-1 # (44)
f = a/u
r = a*(-a).shift(2) # (45)
g = (-a).shift(1) # (47)
fr = .5*u*g**-1. # (46)
if self.aspherics:
# FIXME: not curve/conic
u = self.aspherics
r0 = r
for i in range(len(u)): # (28)
df = S()
for uj in reversed(u):
df = df.shift(uj*np.sign(self.offset[2]))*r
# FIXME: real Newton Raphson
r = r0 + df*(2*k + df*p)
dfr = S()
for i in reversed(range(len(u))):
dfr = (dfr*r).shift((i + 1)*u[i]*np.sign(self.offset[2]))
# FIXME
f += df
fr += dfr
g = (4*r*dfr*dfr).shift(1)**-.5
return r, f, fr, g
def wheelEvent(self, event): self.screen.data.offsetCurrentFrame(numpy.sign(event.delta()) * int(self.screen.parameterDialog.getScrollSpeed()))
def alpha019(self):
return ((-1 * sign((self.close - delay(self.close, 7)) + delta(self.close, 7))) *
(1 + rank(1 + ts_sum(self.returns, 250))))
return np.dot(map_func(x1), map_func(x2).T)
# fit SVM with custom non-linear kernel
model = svm.SVC(kernel=my_kernel, C=1000, tol=1e-3)
model.fit(X,Y)
# coef : dual coefficients alpha_i*y_i per support vector in decision function
coef = model.dual_coef_[0]
# intercept in decision function
b = model.intercept_
# bug in sklearn versions before 0.16: https://github.com/scikit-learn/scikit-learn/issues/4262
# The first class to appear in the dataset, i.e. Y[0], is mapped to the class +1, even if it is labeled -1
version_check = re.match(r"0\.(\d+).", sklearn.__version__).group(1)
if np.sign(Y[0]) == -1 and int(version_check) < 16:
coef = -coef
def calc_plane_norm(sv, coef):
"""
Calculate the normal to the hyperplane (in mapped space)
sv : matrix, contains mapped points, shape = [n_supportvectors, n_mappedfeatures]
coef : array of floats, shape = [n_supportvectors, 1]
"""
components = coef[:, np.newaxis]*sv
return np.sum(components, axis = 0)
def calc_z_plane(x):
"""
Calculate z-coordinates of the decision plane
def generate_2D_edge_meshes(path, closed=False, limit=3, bevel=False):
"""Determines the triangulation of a path in 2D. The resulting `offsets`
can be multiplied by a `width` scalar and be added to the resulting
`centers` to generate the vertices of the triangles for the triangulation,
i.e. `vertices = centers + width*offsets`. Using the `centers` and
`offsets` representation thus allows for the computed triangulation to be
independent of the line width.
Parameters
----------
path : np.ndarray
Nx2 or Nx3 array of central coordinates of path to be triangulated
closed : bool
Bool which determines if the path is closed or not
limit : float
Miter limit which determines when to switch from a miter join to a
bevel join
bevel : bool
Bool which if True causes a bevel join to always be used. If False
a bevel join will only be used when the miter limit is exceeded
Returns
-------
centers : np.ndarray
Mx2 or Mx3 array central coordinates of path trinagles.
offsets : np.ndarray
Mx2 or Mx3 array of the offsets to the central coordinates that need to
be scaled by the line width and then added to the centers to
generate the actual vertices of the triangulation
triangles : np.ndarray
Px3 array of the indices of the vertices that will form the
triangles of the triangulation
"""
clean_path = np.array(path).astype(float)
if closed:
if np.all(clean_path[0] == clean_path[-1]) and len(clean_path) > 2:
clean_path = clean_path[:-1]
full_path = np.concatenate(
([clean_path[-1]], clean_path, [clean_path[0]]), axis=0)
normals = [
segment_normal(full_path[i], full_path[i + 1])
for i in range(len(clean_path))
]
normals = np.array(normals)
full_path = np.concatenate((clean_path, [clean_path[0]]), axis=0)
full_normals = np.concatenate((normals, [normals[0]]), axis=0)
else:
full_path = np.concatenate((clean_path, [clean_path[-2]]), axis=0)
normals = [
segment_normal(full_path[i], full_path[i + 1])
for i in range(len(clean_path))
]
normals[-1] = -normals[-1]
normals = np.array(normals)
full_path = clean_path
full_normals = np.concatenate(([normals[0]], normals), axis=0)
miters = np.array(
[full_normals[i:i + 2].mean(axis=0) for i in range(len(full_path))])
miters = np.array([
miters[i] / np.dot(miters[i], full_normals[i])
if np.dot(miters[i], full_normals[i]) != 0 else full_normals[i]
for i in range(len(full_path))
])
miter_lengths = np.linalg.norm(miters, axis=1)
miters = 0.5 * miters
vertex_offsets = []
central_path = []
triangles = []
m = 0
for i in range(len(full_path)):
if i == 0:
if (bevel or miter_lengths[i] > limit) and closed:
offset = np.array([miters[i, 1], -miters[i, 0]])
offset = 0.5 * offset / np.linalg.norm(offset)
flip = np.sign(np.dot(offset, full_normals[i]))
vertex_offsets.append(offset)
vertex_offsets.append(-flip * miters[i] / miter_lengths[i] *
limit)
vertex_offsets.append(-offset)
central_path.append(full_path[i])
central_path.append(full_path[i])
central_path.append(full_path[i])
triangles.append([0, 1, 2])
m = m + 1
else:
vertex_offsets.append(-miters[i])
vertex_offsets.append(miters[i])
central_path.append(full_path[i])
central_path.append(full_path[i])
elif i == len(full_path) - 1:
if closed:
a = vertex_offsets[m + 1]
b = vertex_offsets[1]
ray = full_path[i] - full_path[i - 1]
if np.cross(a, ray) * np.cross(b, ray) > 0:
triangles.append([m, m + 1, 1])
triangles.append([m, 0, 1])
else:
triangles.append([m, m + 1, 1])
triangles.append([m + 1, 0, 1])
else:
vertex_offsets.append(-miters[i])
vertex_offsets.append(miters[i])
central_path.append(full_path[i])
central_path.append(full_path[i])
a = vertex_offsets[m + 1]
b = vertex_offsets[m + 3]
ray = full_path[i] - full_path[i - 1]
if np.cross(a, ray) * np.cross(b, ray) > 0:
triangles.append([m, m + 1, m + 3])
triangles.append([m, m + 2, m + 3])
else:
triangles.append([m, m + 1, m + 3])
triangles.append([m + 1, m + 2, m + 3])
elif bevel or miter_lengths[i] > limit:
offset = np.array([miters[i, 1], -miters[i, 0]])
offset = 0.5 * offset / np.linalg.norm(offset)
flip = np.sign(np.dot(offset, full_normals[i]))
vertex_offsets.append(offset)
vertex_offsets.append(-flip * miters[i] / miter_lengths[i] * limit)
vertex_offsets.append(-offset)
central_path.append(full_path[i])
central_path.append(full_path[i])
central_path.append(full_path[i])
a = vertex_offsets[m + 1]
b = vertex_offsets[m + 3]
ray = full_path[i] - full_path[i - 1]
if np.cross(a, ray) * np.cross(b, ray) > 0:
triangles.append([m, m + 1, m + 3])
triangles.append([m, m + 2, m + 3])
else:
triangles.append([m, m + 1, m + 3])
triangles.append([m + 1, m + 2, m + 3])
triangles.append([m + 2, m + 3, m + 4])
m = m + 3
else:
vertex_offsets.append(-miters[i])
vertex_offsets.append(miters[i])
central_path.append(full_path[i])
central_path.append(full_path[i])
a = vertex_offsets[m + 1]
b = vertex_offsets[m + 3]
ray = full_path[i] - full_path[i - 1]
if np.cross(a, ray) * np.cross(b, ray) > 0:
triangles.append([m, m + 1, m + 3])
triangles.append([m, m + 2, m + 3])
else:
triangles.append([m, m + 1, m + 3])
triangles.append([m + 1, m + 2, m + 3])
m = m + 2
centers = np.array(central_path)
offsets = np.array(vertex_offsets)
triangles = np.array(triangles)
return centers, offsets, triangles
def reward(self, reward):
"""Bin reward to {+1, 0, -1} by its sign."""
return np.sign(reward)
def predict(self, features):
# sign(x.w + b)
classification = np.sign(np.dot(np.array(features), self.w) + self.b)
return classification
segment_prod = utils.copy_docstring(
'tf.math.segment_prod',
_segment_prod)
segment_sum = utils.copy_docstring(
'tf.math.segment_sum',
_segment_sum)
sigmoid = utils.copy_docstring(
'tf.math.sigmoid',
lambda x, name=None: scipy_special.expit(x))
sign = utils.copy_docstring(
'tf.math.sign',
lambda x, name=None: np.sign(x))
sin = utils.copy_docstring(
'tf.math.sin',
lambda x, name=None: np.sin(x))
sinh = utils.copy_docstring(
'tf.math.sinh',
lambda x, name=None: np.sinh(x))
softmax = utils.copy_docstring(
'tf.math.softmax',
_softmax)
def _softplus(x, name=None): # pylint: disable=unused-argument
def alpha030(self):
delta_close = delta(self.close, 1)
inner = sign(delta_close) + sign(delay(delta_close, 1)) + sign(delay(delta_close, 2))
return ((1.0 - rank(inner)) * ts_sum(self.volume, 5)) / ts_sum(self.volume, 20)
def alpha007(self):
adv20 = sma(self.volume, 20)
alpha = -1 * ts_rank(abs(delta(self.close, 7)), 60) * sign(delta(self.close, 7))
alpha[adv20 >= self.volume] = -1
return alpha
def alpha012(self):
return sign(delta(self.volume, 1)) * (-1 * delta(self.close, 1))
def projSO3(M): # return a rotation matrix close to M
Q,R = np.linalg.qr(M)
return Q@diag(diag(sign(R)))
def __updateParameters(self, x, y):
"""
w_k = w_{k-1} - lr*dw
b_k = b_{k-1} - lr*db
"""
yt = self.__fit_predict(x)
residuals = yt - y
dW = 2/self.m * (x.T).dot(residuals) + self.lambda_ * (self.W != 0).astype(int)*np.sign(self.W)
self.dws.append(dW)
self.W = self.W - self.lr*dW
if self.withBias:
dB = 2/self.m * np.sum( residuals )
self.B = self.B - self.lr*dB
theta_train = np.zeros(35, dtype=float)
converged = False
train_accuracy = 0
train_iter = 1000
eta = 0.05
error_train = []
error_test = []
while (converged != True and train_iter > 0):
converged = True
train_accuracy = len(data_train)
sum = np.zeros(35, dtype=float)
for i in range(len(data_train)):
inner_product = np.inner(data_train[i], theta_train)
result = int(np.sign(inner_product))
if (result < 0 and label_train[i]
== 1) or (result > 0 and label_train[i] == 0) or result == 0:
train_accuracy -= 1
converged = False
error_train.append(1 - train_accuracy / float(len(data_train)))
test_accuracy = len(data_test)
for i in range(len(data_test)):
inner_product = np.inner(data_test[i], theta_train)
result = int(np.sign(inner_product))
if (result < 0 and label_test[i] == 1) or (result > 0 and label_test[i]
== 0) or result == 0:
test_accuracy -= 1
error_test.append(1 - test_accuracy / float(len(data_test)))
def test_sanity_check_pls_canonical_random():
# Sanity check for PLSCanonical on random data
# The results were checked against the R-package plspm
n = 500
p_noise = 10
q_noise = 5
# 2 latents vars:
rng = check_random_state(11)
l1 = rng.normal(size=n)
l2 = rng.normal(size=n)
latents = np.array([l1, l1, l2, l2]).T
X = latents + rng.normal(size=4 * n).reshape((n, 4))
Y = latents + rng.normal(size=4 * n).reshape((n, 4))
X = np.concatenate((X, rng.normal(size=p_noise * n).reshape(n, p_noise)),
axis=1)
Y = np.concatenate((Y, rng.normal(size=q_noise * n).reshape(n, q_noise)),
axis=1)
pls = PLSCanonical(n_components=3)
pls.fit(X, Y)
expected_x_weights = np.array([
[0.65803719, 0.19197924, 0.21769083],
[0.7009113, 0.13303969, -0.15376699],
[0.13528197, -0.68636408, 0.13856546],
[0.16854574, -0.66788088, -0.12485304],
[-0.03232333, -0.04189855, 0.40690153],
[0.1148816, -0.09643158, 0.1613305],
[0.04792138, -0.02384992, 0.17175319],
[-0.06781, -0.01666137, -0.18556747],
[-0.00266945, -0.00160224, 0.11893098],
[-0.00849528, -0.07706095, 0.1570547],
[-0.00949471, -0.02964127, 0.34657036],
[-0.03572177, 0.0945091, 0.3414855],
[0.05584937, -0.02028961, -0.57682568],
[0.05744254, -0.01482333, -0.17431274],
])
expected_x_loadings = np.array([
[0.65649254, 0.1847647, 0.15270699],
[0.67554234, 0.15237508, -0.09182247],
[0.19219925, -0.67750975, 0.08673128],
[0.2133631, -0.67034809, -0.08835483],
[-0.03178912, -0.06668336, 0.43395268],
[0.15684588, -0.13350241, 0.20578984],
[0.03337736, -0.03807306, 0.09871553],
[-0.06199844, 0.01559854, -0.1881785],
[0.00406146, -0.00587025, 0.16413253],
[-0.00374239, -0.05848466, 0.19140336],
[0.00139214, -0.01033161, 0.32239136],
[-0.05292828, 0.0953533, 0.31916881],
[0.04031924, -0.01961045, -0.65174036],
[0.06172484, -0.06597366, -0.1244497],
])
expected_y_weights = np.array([
[0.66101097, 0.18672553, 0.22826092],
[0.69347861, 0.18463471, -0.23995597],
[0.14462724, -0.66504085, 0.17082434],
[0.22247955, -0.6932605, -0.09832993],
[0.07035859, 0.00714283, 0.67810124],
[0.07765351, -0.0105204, -0.44108074],
[-0.00917056, 0.04322147, 0.10062478],
[-0.01909512, 0.06182718, 0.28830475],
[0.01756709, 0.04797666, 0.32225745],
])
expected_y_loadings = np.array([
[0.68568625, 0.1674376, 0.0969508],
[0.68782064, 0.20375837, -0.1164448],
[0.11712173, -0.68046903, 0.12001505],
[0.17860457, -0.6798319, -0.05089681],
[0.06265739, -0.0277703, 0.74729584],
[0.0914178, 0.00403751, -0.5135078],
[-0.02196918, -0.01377169, 0.09564505],
[-0.03288952, 0.09039729, 0.31858973],
[0.04287624, 0.05254676, 0.27836841],
])
assert_array_almost_equal(np.abs(pls.x_loadings_),
np.abs(expected_x_loadings))
assert_array_almost_equal(np.abs(pls.x_weights_),
np.abs(expected_x_weights))
assert_array_almost_equal(np.abs(pls.y_loadings_),
np.abs(expected_y_loadings))
assert_array_almost_equal(np.abs(pls.y_weights_),
np.abs(expected_y_weights))
x_loadings_sign_flip = np.sign(pls.x_loadings_ / expected_x_loadings)
x_weights_sign_flip = np.sign(pls.x_weights_ / expected_x_weights)
y_weights_sign_flip = np.sign(pls.y_weights_ / expected_y_weights)
y_loadings_sign_flip = np.sign(pls.y_loadings_ / expected_y_loadings)
assert_array_almost_equal(x_loadings_sign_flip, x_weights_sign_flip)
assert_array_almost_equal(y_loadings_sign_flip, y_weights_sign_flip)
assert_matrix_orthogonal(pls.x_weights_)
assert_matrix_orthogonal(pls.y_weights_)
assert_matrix_orthogonal(pls._x_scores)
assert_matrix_orthogonal(pls._y_scores)
X_2 = np.array([[-1, 0, 1.5, -0.5]]).T
X_3 = np.array([[-1, -1, 1, 0.5]]).T
X = np.append(X_1, X_2, axis=1)
X = np.append(X, X_3, axis=1) #X是4*3的矩阵
Y = np.array([[-1, -1, 1]]) #Y是1*3的矩阵
W = (np.random.random([4, 1]) - 0.5) * 2 #初始化权值，范围是[-1,1]
#W是4*1的矩阵
lr = 0.11 #设置学习率为0.11
output = np.array([[0, 0, 0]]) #神经网络的输出(output)初始化为0
def iteration(): #迭代函数
global X, Y, W, lr
output = np.sign(np.dot(W.T, X))
delta = lr * (np.dot(X, (Y - output).T))
W = W + delta #对权值矩阵进行调整
for i in range(100):
print("第 %s 次迭代" % i)
print(W)
iteration()
output = np.sign(np.dot(W.T, X)) #计算当前的输出
if (output == Y).all():
print("Finished")
break
def iteration(): #迭代函数
global X, Y, W, lr
output = np.sign(np.dot(W.T, X))
delta = lr * (np.dot(X, (Y - output).T))
W = W + delta #对权值矩阵进行调整
# This block should reset the PID if there is nothing on the screen for more than 5 frames.
if mX == 0:
count += 1
else:
count = 0
if count > 5:
p = PID(3.0, 0.4, 1.2)
p.setPoint(300.0)
# Note: need to put in something so it doesn't send PID controls when mX is 0
pid = p.update(mX)
if abs(pid) > 255:
pid = 255 * (np.sign(pid))
if abs(pid) < 100:
pid = 100 * (np.sign(pid))
val = str(int(abs(pid)))
if pid > 0:
motControl = '-' + val + '-' + val + '+' + val + '+' + val + '>'
else:
motControl = '+' + val + '+' + val + '-' + val + '-' + val + '>'
if mX != 0:
ser.write(motControl)
cv2.putText(frame, str(pid),
# %% # Resta np.subtract(arr_2, arr_1) # %% # Raiz cuadrada np.sqrt(arr_1) # %% # Potencia np.power(arr_1, 2) # %% # Signo np.sign(arr_1) # %% # Seno np.sin(arr_1) # %% # CoSeno np.cos(arr_1) # %% # Tangente np.tan(arr_1) # %% # Grados a radianes
def _step(self, action):
#if action[0]==action[1]==action[2]==action[3]==0.0:
# self.hull.ApplyForceToCenter((0, 10.0*(5.0*(20/SCALE)**2)), True)
# self.legs[0].ApplyForceToCenter((0, 10.0 * 1.0 * LEG_H*LEG_W), True)
# self.legs[1].ApplyForceToCenter((0, 10.0 * 1.0 * LEG_H * LEG_W), True)
# self.legs[2].ApplyForceToCenter((0, 10.0 * 1.0 * LEG_H * LEG_W), True)
# self.legs[3].ApplyForceToCenter((0, 10.0 * 1.0 * LEG_H * LEG_W), True)
#self.hull.ApplyForceToCenter((0, 20), True) #-- Uncomment this to receive a bit of stability help
control_speed = False
if control_speed:
self.joints[0].motorSpeed = float(SPEED_HIP * np.clip(action[0], -1, 1))
self.joints[1].motorSpeed = float(SPEED_KNEE * np.clip(action[1], -1, 1))
self.joints[2].motorSpeed = float(SPEED_HIP * np.clip(action[2], -1, 1))
self.joints[3].motorSpeed = float(SPEED_KNEE * np.clip(action[3], -1, 1))
else:
self.joints[0].maxMotorTorque = float(np.abs(action[0]))
self.joints[0].motorSpeed = float(SPEED_HIP * np.sign(action[0]))
self.joints[1].maxMotorTorque = float(np.abs(action[1]))
self.joints[1].motorSpeed = float(SPEED_KNEE * np.sign(action[1]))
self.joints[2].maxMotorTorque = float(np.abs(action[2]))
self.joints[2].motorSpeed = float(SPEED_HIP * np.sign(action[2]))
self.joints[3].maxMotorTorque = float(np.abs(action[3]))
self.joints[3].motorSpeed = float(SPEED_KNEE * np.sign(action[3]))
self.world.Step(1.0/FPS, 6*30, 2*30)
pos = self.hull.position
vel = self.hull.linearVelocity
for i in range(10):
self.lidar[i].fraction = 1.0
self.lidar[i].p1 = pos
self.lidar[i].p2 = (
pos[0] + math.sin(1.5*i/10.0)*LIDAR_RANGE,
pos[1] - math.cos(1.5*i/10.0)*LIDAR_RANGE)
self.world.RayCast(self.lidar[i], self.lidar[i].p1, self.lidar[i].p2)
state = [
self.hull.angle, #s[0]
self.hull.angularVelocity, #s[1]
vel.x, #s[2] global dxcom
vel.y, #s[3]
self.joints[0].angle, #s[4]
self.joints[0].speed, #s[5]
self.joints[1].angle, #s[6]
self.joints[1].speed, #s[7]
1.0 if self.legs[1].ground_contact else 0.0, #s[8]
self.joints[2].angle, #s[9]
self.joints[2].speed, #s[10]
self.joints[3].angle, #s[11]
self.joints[3].speed, #s[12]
1.0 if self.legs[3].ground_contact else 0.0, #s[13]
pos.y - TERRAIN_HEIGHT, #s[14]
pos.x - self.legs[1].position.x, # s[15]
pos.x - self.legs[3].position.x, # s[16]
pos.x - init_x, # global xcom s[17]
]
state += [l.fraction for l in self.lidar]
assert len(state)==28
self.scroll = pos.x - VIEWPORT_W/SCALE/5
shaping = 130*pos[0]/SCALE # moving forward is a way to receive reward (normalized to get 300 on completion)
shaping -= 5.0*abs(state[0]) # keep head straight, other than that and falling, any behavior is unpunished
reward = 0
if self.prev_shaping is not None:
reward = shaping - self.prev_shaping
self.prev_shaping = shaping
for a in action:
reward -= 0.00035 * MOTORS_TORQUE * np.clip(np.abs(a), 0, 1)
# normalized to about -50.0 using heuristic, more optimal agent should spend less
done = False
if self.game_over or pos[0] < 0:
reward = -100
done = True
if pos[0] > (TERRAIN_LENGTH-TERRAIN_GRASS)*TERRAIN_STEP:
done = True
return np.array(state), reward, done, {}
def _SMC_acc_Cartesian(self,sigma_max,sigma_d_max,sigma_min,sigma_d_min,phi_max,phi_min,J,dsigma_dp,Ka,U_plus,pd_ref,pdd_ref,pose,p_ref,Kp,Kpd,Kqd,Kq,Fx_max,Fy_max,U_plus_map,mode_eq,mode_ineq,temp_int,nContact,sm_ant,ASG_opt,U_plus_map2,auto,U_plus2,U_plus2_map,U_plus2_map2,phi2_max,Sensor2R,Kp_L3,Kpd_L3,threshold,error,Tp):
p = np.zeros((6,1))
joint_acc_4 = np.zeros((7,1))
dsigma_dp2 = np.zeros((6,6))
phi_tmp_L2 = np.sign(phi2_max)
XYZ = pose['position']
RPY = Quat2rpy(pose['orientation'])
Qd = self._limb.joint_velocities()
Q = self._limb.joint_angles()
q = np.zeros((7,1))
qo = np.zeros((7,1))
qd = np.zeros((7,1))
for i in range(0,6,1):
if i < 3:
p[i] = XYZ[i]
else:
p[i] = RPY[i-3]
if p[3] < 0:
p[3] = p[3] + 2.0*math.pi
for i,joint in enumerate(self._limb.joint_names()):
qo[i] = self._start_angles[joint]
q[i] = (self._limb.joint_angles())[joint]
qd[i] = Qd[joint]
pd = J.dot(qd)
#Saturation of the feedforward error
for i in range(0,6):
error[i] = p_ref[i] - p[i]
#Error saturation by direction
m_e = np.sqrt(error[0]*error[0] + error[1]*error[1] + error[2]*error[2])
if m_e > 0.025:
for i in range(0,3):
error[i] = error[i]/m_e*0.025
#Error saturation by module
#for i in range(0,3):
# if np.abs(p_ref[i] - p[i]) > 0.02:
# error[i] = np.sign(p_ref[i] - p[i])*0.02
# else:
# error[i] = p_ref[i] - p[i]
phi_tmp = np.sign(phi_max)
#if phi_max[1] > 0:
# phi_tmp[1] = 1.0
# dsigma_dp[1][1] = 4.0
#elif phi_min[1] > 0:
# phi_tmp[1] = -1.0
# dsigma_dp[1][1] = 4.0
#else:
# dsigma_dp[1][1] = 0.0
# phi_tmp[1] = 0.0
#if phi_max[0] > 0:
# phi_tmp[0] = 1.0
# dsigma_dp[0][0] = 4.0
#elif phi_min[0] > 0:
# phi_tmp[0] = -1.0
# dsigma_dp[0][0] = 4.0
#else:
# dsigma_dp[0][0] = 0.0
# phi_tmp[0] = 0.0
#1st Level SMC:
joint_acc = -linalg.pinv(Ka*(dsigma_dp.T).dot(J),rcond = 0.01).dot(U_plus_map2.dot(phi_tmp))
#2nd Level trajectory tracking:
# Automatic or Collaborative mode
if auto == 1 or nContact == True:
N2 = np.eye(7) - linalg.pinv((dsigma_dp.T).dot(J),rcond = 0.01).dot((dsigma_dp.T).dot(J))
joint_acc_2 = linalg.pinv(J.dot(N2),rcond = 0.01).dot(pdd_ref + Kpd*(pd_ref - pd) + Kp*error - J.dot(joint_acc))
#3rf Level self motion:
N3 = N2.dot(np.eye(7) - np.linalg.pinv(J.dot(N2)).dot(J.dot(N2)))
joint_acc_3 = linalg.pinv(N3,rcond = 0.01).dot(-Kqd*qd + Kq*(qo - q) - (joint_acc + joint_acc_2))
self._joint_vel = self._joint_vel + (joint_acc + joint_acc_2 + joint_acc_3)/self._rate
elif auto == 0 or nContact == False:
# Level 2: X, Y, Z inequalities activation
if phi2_max[0] > 0:
phi_tmp_L2[0] = 1.0
dsigma_dp2[0][0] = (2.0*Sensor2R[0])/(2.0*np.sqrt(Sensor2R[0]*Sensor2R[0] + Sensor2R[1]*Sensor2R[1]))
dsigma_dp2[1][0] = (2.0*Sensor2R[1])/(2.0*np.sqrt(Sensor2R[0]*Sensor2R[0] + Sensor2R[1]*Sensor2R[1]))
else:
phi_tmp_L2[0] = 0.0
dsigma_dp2[0][0] = 0.0
dsigma_dp2[1][0] = 0.0
#print "phi2_max[0]: ",np.sign(phi2_max[0])
# Yaw control in Level 2 for Exp 1
#if phi2_max[5] > 0:
# phi_tmp_L2[5] = 1.0
# dsigma_dp2[5][5] = np.sign(Sensor2R[5])
#else:
# phi_tmp_L2[5] = 0.0
# dsigma_dp2[5][5] = 0.0
U_plus2_map2[0][0] = U_plus2_map[0][0]*U_plus2
#U_plus_map2[5][5] = U_plus_map[5][5]*U_plus
N2 = np.eye(7) - linalg.pinv((dsigma_dp.T).dot(J),rcond = 0.01).dot((dsigma_dp.T).dot(J))
joint_acc_2 = -linalg.pinv(((dsigma_dp2.T).dot(J)).dot(N2),rcond = 0.01).dot((np.sign(phi_tmp_L2.T).dot(U_plus2_map2)).T + J.dot(joint_acc))
#3rd Level cartesian self motion:
#N3 = N2.dot(np.eye(7) - np.linalg.pinv(((dsigma_dp2.T).dot(J)).dot(N2)).dot(((dsigma_dp2.T).dot(J)).dot(N2)))
#joint_acc_3 = linalg.pinv(J.dot(N3),rcond = 0.01).dot(-np.sign(pd)*Kpd_L3 - Kp_L3*pd - J.dot(joint_acc + joint_acc_2))
N3 = N2.dot(np.eye(7) - np.linalg.pinv(((dsigma_dp2.T).dot(J)).dot(N2)).dot(((dsigma_dp2.T).dot(J)).dot(N2)))
joint_acc_3 = linalg.pinv(J.dot(N3),rcond = 0.01).dot(pdd_ref + Kpd*(pd_ref - pd) + Kp*error + np.sign(pd_ref - pd + Kp/Kpd*error)*Kpd_L3 - J.dot(joint_acc + joint_acc_2))
#if np.sqrt(Sensor2R[0]*Sensor2R[0] + Sensor2R[1]*Sensor2R[1]) > threshold:
# joint_acc_3 = np.zeros((7,1))
#4th Level home configuration:
N4 = N3.dot(np.eye(7) - np.linalg.pinv(J.dot(N3)).dot(J.dot(N3)))
joint_acc_4 = linalg.pinv(N4,rcond = 0.01).dot(-Kqd*qd + Kq*(qo - q) - (joint_acc + joint_acc_2 + joint_acc_3))
self._joint_vel = self._joint_vel + (joint_acc + joint_acc_2 + joint_acc_3 + joint_acc_4)/self._rate
cmd = make_cmd(self._limb.joint_names(), self._joint_vel)
# command new joint torques
self._limb.set_joint_velocities(cmd)
b = ""
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(p.T)),decimals = 4)))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(pd.T)),decimals = 4)))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(error.T)),decimals = 4)))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(qo.T)),decimals = 4)))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(q.T)),decimals = 4)))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(qd.T)),decimals = 4)))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(joint_acc.T)),decimals = 4)))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(joint_acc_2.T)),decimals = 4)))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(joint_acc_3.T)),decimals = 4)))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(joint_acc_4.T)),decimals = 4)))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray((joint_acc + joint_acc_2 + joint_acc_3 + joint_acc_4).T)),decimals = 4)))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(self._joint_vel.T)),decimals = 4)))
b += " " + str(np.round(dsigma_dp[0][0],decimals = 4)) + " " + str(np.round(dsigma_dp[1][1],decimals = 4))
b += " " + str(np.round(dsigma_dp[2][2],decimals = 4)) + " " + str(np.round(dsigma_dp[3][3],decimals = 4))
b += " " + str(np.round(dsigma_dp[4][4],decimals = 4)) + " " + str(np.round(dsigma_dp[5][5],decimals = 4))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(phi_tmp.T)),decimals = 4)))
b += " " + str(np.round(U_plus_map2[2][2],decimals = 4)) + " " + str(np.round(U_plus_map2[3][3],decimals = 4)) + " " + str(np.round(U_plus_map2[4][4],decimals = 4))
b += " " + str(np.round(dsigma_dp2[0][0],decimals = 4)) + " " + str(np.round(dsigma_dp2[1][1],decimals = 4))
b += " " + str(np.round(dsigma_dp2[2][2],decimals = 4)) + " " + str(np.round(dsigma_dp2[3][3],decimals = 4))
b += " " + str(np.round(dsigma_dp2[4][4],decimals = 4)) + " " + str(np.round(dsigma_dp2[5][5],decimals = 4))
b += " " + ' '.join(map(str,np.around(np.squeeze(np.asarray(phi_tmp_L2.T)),decimals = 4)))
b += " " + str(np.round(U_plus2_map2[0][0],decimals = 4))
return b
def _copysign(x1, x2):
"""Slow replacement for np.copysign, which was introduced in numpy 1.4"""
return np.abs(x1) * np.sign(x2)
def _generate_terrain(self, hardcore):
GRASS, STUMP, STAIRS, PIT, _STATES_ = range(5)
state = GRASS
velocity = 0.0
y = TERRAIN_HEIGHT
counter = TERRAIN_STARTPAD
oneshot = False
self.terrain = []
self.terrain_x = []
self.terrain_y = []
for i in range(TERRAIN_LENGTH):
x = i*TERRAIN_STEP
self.terrain_x.append(x)
if state==GRASS and not oneshot:
velocity = 0.8*velocity + 0.01*np.sign(TERRAIN_HEIGHT - y)
if i > TERRAIN_STARTPAD: velocity=0 #+= self.np_random.uniform(-1, 1)/SCALE #1
y += velocity
#y = TERRAIN_HEIGHT
elif state==PIT and oneshot:
counter = self.np_random.randint(3, 5)
poly = [
(x, y),
(x+TERRAIN_STEP, y),
(x+TERRAIN_STEP, y-4*TERRAIN_STEP),
(x, y-4*TERRAIN_STEP),
]
t = self.world.CreateStaticBody(
fixtures = fixtureDef(
shape=polygonShape(vertices=poly),
friction = FRICTION
))
t.color1, t.color2 = (1,1,1), (0.6,0.6,0.6)
self.terrain.append(t)
t = self.world.CreateStaticBody(
fixtures = fixtureDef(
shape=polygonShape(vertices=[(p[0]+TERRAIN_STEP*counter,p[1]) for p in poly]),
friction = FRICTION
))
t.color1, t.color2 = (1,1,1), (0.6,0.6,0.6)
self.terrain.append(t)
counter += 2
original_y = y
elif state==PIT and not oneshot:
y = original_y
if counter > 1:
y -= 4*TERRAIN_STEP
elif state==STUMP and oneshot:
counter = self.np_random.randint(1, 3)
poly = [
(x, y),
(x+counter*TERRAIN_STEP, y),
(x+counter*TERRAIN_STEP, y+counter*TERRAIN_STEP),
(x, y+counter*TERRAIN_STEP),
]
t = self.world.CreateStaticBody(
fixtures = fixtureDef(
shape=polygonShape(vertices=poly),
friction = FRICTION
))
t.color1, t.color2 = (1,1,1), (0.6,0.6,0.6)
self.terrain.append(t)
elif state==STAIRS and oneshot:
stair_height = +1 if self.np_random.rand() > 0.5 else -1
stair_width = self.np_random.randint(4, 5)
stair_steps = self.np_random.randint(3, 5)
original_y = y
for s in range(stair_steps):
poly = [
(x+( s*stair_width)*TERRAIN_STEP, y+( s*stair_height)*TERRAIN_STEP),
(x+((1+s)*stair_width)*TERRAIN_STEP, y+( s*stair_height)*TERRAIN_STEP),
(x+((1+s)*stair_width)*TERRAIN_STEP, y+(-1+s*stair_height)*TERRAIN_STEP),
(x+( s*stair_width)*TERRAIN_STEP, y+(-1+s*stair_height)*TERRAIN_STEP),
]
t = self.world.CreateStaticBody(
fixtures = fixtureDef(
shape=polygonShape(vertices=poly),
friction = FRICTION
))
t.color1, t.color2 = (1,1,1), (0.6,0.6,0.6)
self.terrain.append(t)
counter = stair_steps*stair_width
elif state==STAIRS and not oneshot:
s = stair_steps*stair_width - counter - stair_height
n = s/stair_width
y = original_y + (n*stair_height)*TERRAIN_STEP
oneshot = False
self.terrain_y.append(y)
counter -= 1
if counter==0:
counter = self.np_random.randint(TERRAIN_GRASS/2, TERRAIN_GRASS)
if state==GRASS and hardcore:
state = self.np_random.randint(1, _STATES_)
oneshot = True
else:
state = GRASS
oneshot = True
self.terrain_poly = []
for i in range(TERRAIN_LENGTH-1):
poly = [
(self.terrain_x[i], self.terrain_y[i]),
(self.terrain_x[i+1], self.terrain_y[i+1])
]
t = self.world.CreateStaticBody(
fixtures = fixtureDef(
shape=edgeShape(vertices=poly),
friction = FRICTION,
categoryBits=0x0001,
))
color = (0.3, 1.0 if i%2==0 else 0.8, 0.3)
t.color1 = color
t.color2 = color
self.terrain.append(t)
color = (0.4, 0.6, 0.3)
poly += [ (poly[1][0], 0), (poly[0][0], 0) ]
self.terrain_poly.append( (poly, color) )
self.terrain.reverse()
def sign(z):
return np.sign(z)
def step(self, action):
if self.continuous:
action = np.clip(action, -1, +1).astype(np.float32)
else:
assert self.action_space.contains(action), "%r (%s) invalid " % (
action,
type(action),
)
# Engines
tip = (math.sin(self.lander.angle), math.cos(self.lander.angle))
side = (-tip[1], tip[0])
dispersion = [0 for _ in range(2)]
m_power = 0.0
if (self.continuous and action[0] > 0.0) or (
not self.continuous and action == 2
):
# Main engine
if self.continuous:
m_power = (np.clip(action[0], 0.0, 1.0) + 1.0) * 0.5 # 0.5..1.0
assert m_power >= 0.5 and m_power <= 1.0
else:
m_power = 1.0
ox = (
tip[0] * (4 / SCALE + 2 * dispersion[0]) + side[0] * dispersion[1]
) # 4 is move a bit downwards, +-2 for randomness
oy = -tip[1] * (4 / SCALE + 2 * dispersion[0]) - side[1] * dispersion[1]
impulse_pos = (self.lander.position[0] + ox, self.lander.position[1] + oy)
p = self._create_particle(
3.5, # 3.5 is here to make particle speed adequate
impulse_pos[0],
impulse_pos[1],
m_power,
) # particles are just a decoration
p.ApplyLinearImpulse(
(ox * MAIN_ENGINE_POWER * m_power, oy * MAIN_ENGINE_POWER * m_power),
impulse_pos,
True,
)
self.lander.ApplyLinearImpulse(
(-ox * MAIN_ENGINE_POWER * m_power, -oy * MAIN_ENGINE_POWER * m_power),
impulse_pos,
True,
)
s_power = 0.0
if (self.continuous and np.abs(action[1]) > 0.5) or (
not self.continuous and action in [1, 3]
):
# Orientation engines
if self.continuous:
direction = np.sign(action[1])
s_power = np.clip(np.abs(action[1]), 0.5, 1.0)
assert s_power >= 0.5 and s_power <= 1.0
else:
direction = action - 2
s_power = 1.0
ox = tip[0] * dispersion[0] + side[0] * (
3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE
)
oy = -tip[1] * dispersion[0] - side[1] * (
3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE
)
impulse_pos = (
self.lander.position[0] + ox - tip[0] * 17 / SCALE,
self.lander.position[1] + oy + tip[1] * SIDE_ENGINE_HEIGHT / SCALE,
)
p = self._create_particle(0.7, impulse_pos[0], impulse_pos[1], s_power)
p.ApplyLinearImpulse(
(ox * SIDE_ENGINE_POWER * s_power, oy * SIDE_ENGINE_POWER * s_power),
impulse_pos,
True,
)
self.lander.ApplyLinearImpulse(
(-ox * SIDE_ENGINE_POWER * s_power, -oy * SIDE_ENGINE_POWER * s_power),
impulse_pos,
True,
)
self.world.Step(1.0 / FPS, 6 * 30, 2 * 30)
pos = self.lander.position
vel = self.lander.linearVelocity
state = [
(pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),
(pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),
vel.x * (VIEWPORT_W / SCALE / 2) / FPS,
vel.y * (VIEWPORT_H / SCALE / 2) / FPS,
self.lander.angle,
20.0 * self.lander.angularVelocity / FPS,
1.0 if self.legs[0].ground_contact else 0.0,
1.0 if self.legs[1].ground_contact else 0.0,
]
assert len(state) == 8
reward = 0
shaping = (
-100 * np.sqrt(state[0] * state[0] + state[1] * state[1])
- 100 * np.sqrt(state[2] * state[2] + state[3] * state[3])
- 100 * abs(state[4])
+ 10 * state[6]
+ 10 * state[7]
) # And ten points for legs contact, the idea is if you
# lose contact again after landing, you get negative reward
if self.prev_shaping is not None:
reward = shaping - self.prev_shaping
self.prev_shaping = shaping
reward -= (
m_power * 0.30
) # less fuel spent is better, about -30 for heuristic landing
reward -= s_power * 0.03
done = False
if self.game_over or abs(state[0]) >= 1.0:
done = True
reward = -100
if not self.lander.awake:
done = True
reward = +100
return np.array(state, dtype=np.float32), reward, done, {}
def backward(self, x):
return 0.5 * (np.sign(x) + 1)
def scores(key, paths, config):
values = [mapreduce.OutputCollector(p) for p in paths]
try:
values = [item.load() for item in values]
except Exception as e:
print(e)
return None
y_true_splits = [item["y_true"].ravel() for item in values]
y_pred_splits = [item["y_pred"].ravel() for item in values]
y_true = np.concatenate(y_true_splits)
y_pred = np.concatenate(y_pred_splits)
prob_pred_splits = [item["prob_pred"].ravel() for item in values]
prob_pred = np.concatenate(prob_pred_splits)
# Prediction performances
p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None)
auc = roc_auc_score(y_true, prob_pred)
# balanced accuracy (recall_mean)
bacc_splits = [recall_score(y_true_splits[f], y_pred_splits[f], average=None).mean() for f in range(len(y_true_splits))]
auc_splits = [roc_auc_score(y_true_splits[f], prob_pred_splits[f]) for f in range(len(y_true_splits))]
print("bacc all - mean(bacc) %.3f" % (r.mean() - np.mean(bacc_splits)))
# P-values
success = r * s
success = success.astype('int')
prob_class1 = np.count_nonzero(y_true) / float(len(y_true))
pvalue_recall0_true_prob = binom_test(success[0], s[0], 1 - prob_class1,alternative = 'greater')
pvalue_recall1_true_prob = binom_test(success[1], s[1], prob_class1,alternative = 'greater')
pvalue_recall0_unknwon_prob = binom_test(success[0], s[0], 0.5,alternative = 'greater')
pvalue_recall1_unknown_prob = binom_test(success[1], s[1], 0.5,alternative = 'greater')
pvalue_bacc = binom_test(success[0]+success[1], s[0] + s[1], p=0.5,alternative = 'greater')
# Beta's measures of similarity
betas = np.hstack([item["beta"][:, penalty_start:].T for item in values]).T
# Correlation
R = np.corrcoef(betas)
R = R[np.triu_indices_from(R, 1)]
# Fisher z-transformation / average
z_bar = np.mean(1. / 2. * np.log((1 + R) / (1 - R)))
# bracktransform
r_bar = (np.exp(2 * z_bar) - 1) / (np.exp(2 * z_bar) + 1)
# threshold betas to compute fleiss_kappa and DICE
try:
betas_t = np.vstack([
array_utils.arr_threshold_from_norm2_ratio(betas[i, :], .99)[0]
for i in range(betas.shape[0])])
# Compute fleiss kappa statistics
beta_signed = np.sign(betas_t)
table = np.zeros((beta_signed.shape[1], 3))
table[:, 0] = np.sum(beta_signed == 0, 0)
table[:, 1] = np.sum(beta_signed == 1, 0)
table[:, 2] = np.sum(beta_signed == -1, 0)
fleiss_kappa_stat = fleiss_kappa(table)
# Paire-wise Dice coeficient
ij = [[i, j] for i in range(betas.shape[0]) for j in range(i+1, betas.shape[0])]
dices = list()
for idx in ij:
A, B = beta_signed[idx[0], :], beta_signed[idx[1], :]
dices.append(float(np.sum((A == B)[(A != 0) & (B != 0)])) / (np.sum(A != 0) + np.sum(B != 0)))
dice_bar = np.mean(dices)
except:
dice_bar = fleiss_kappa_stat = 0
# Proportion of selection within the support accross the CV
support_count = (betas_t != 0).sum(axis=0)
support_count = support_count[support_count > 0]
support_prop = support_count / betas_t.shape[0]
scores = OrderedDict()
scores['key'] = key
scores['recall_0'] = r[0]
scores['recall_1'] = r[1]
scores['bacc'] = r.mean()
scores['bacc_se'] = np.std(bacc_splits) / np.sqrt(len(bacc_splits))
scores["auc"] = auc
scores['auc_se'] = np.std(auc_splits) / np.sqrt(len(auc_splits))
scores['pvalue_recall0_true_prob_one_sided'] = pvalue_recall0_true_prob
scores['pvalue_recall1_true_prob_one_sided'] = pvalue_recall1_true_prob
scores['pvalue_recall0_unknwon_prob_one_sided'] = pvalue_recall0_unknwon_prob
scores['pvalue_recall1_unknown_prob_one_sided'] = pvalue_recall1_unknown_prob
scores['pvalue_bacc_mean'] = pvalue_bacc
scores['prop_non_zeros_mean'] = float(np.count_nonzero(betas_t)) / \
float(np.prod(betas.shape))
scores['beta_r_bar'] = r_bar
scores['beta_fleiss_kappa'] = fleiss_kappa_stat
scores['beta_dice_bar'] = dice_bar
scores['beta_dice'] = str(dices)
scores['beta_r'] = str(R)
scores['beta_support_prop_select_mean'] = support_prop.mean()
scores['beta_support_prop_select_sd'] = support_prop.std()
return scores
def start_PID(self, input=None):
try:
for sig in self.move_done_signals:
sig.connect(self.move_done)
for sig in self.grab_done_signals:
sig.connect(self.det_done)
self.current_time = time.perf_counter()
self.status_sig.emit(["Update_Status", 'PID loop starting', 'log'])
while self.running:
#print('input: {}'.format(self.input))
## GRAB DATA FIRST AND WAIT ALL DETECTORS RETURNED
self.det_done_flag = False
self.det_done_datas = OrderedDict()
for ind_det, cmd in enumerate(self.detector_modules_commands):
cmd.emit(
ThreadCommand("single",
[self.det_averaging[ind_det], '']))
QtWidgets.QApplication.processEvents()
self.wait_for_det_done()
self.input = self.model_class.convert_input(
self.det_done_datas)
## EXECUTE THE PID
output = self.pid(self.input)
## PROCESS THE OUTPUT IF NEEDED
if output is not None and self.output is not None:
if self.filter['enable']:
if np.abs(output -
self.output) <= self.filter['value']:
self.output = output
else:
self.output += np.abs(
self.filter['value']) * np.sign(output -
self.output)
self.pid._last_output = self.output
else:
self.output = output
else:
self.output = output
## APPLY THE PID OUTPUT TO THE ACTUATORS
#print('output: {}'.format(self.output))
if self.output is None:
self.output = self.pid.setpoint
dt = time.perf_counter() - self.current_time
self.output_to_actuator = self.model_class.convert_output(
self.output, dt, stab=True)
if not self.paused:
self.move_done_positions = OrderedDict()
for ind_mov, cmd in enumerate(self.move_modules_commands):
cmd.emit(
ThreadCommand('move_Abs',
[self.output_to_actuator[ind_mov]]))
QtWidgets.QApplication.processEvents()
self.wait_for_move_done()
self.current_time = time.perf_counter()
QtWidgets.QApplication.processEvents()
QThread.msleep(int(self.pid.sample_time * 1000))
self.status_sig.emit(["Update_Status", 'PID loop exiting', 'log'])
for sig in self.move_done_signals:
sig.disconnect(self.move_done)
for sig in self.grab_done_signals:
sig.disconnect(self.det_done)
except Exception as e:
self.status_sig.emit(["Update_Status", str(e), 'log'])
def _HypergraphPredictor__eval_i(args):
g_i = args[0]
b = args[1]
phi = args[2]
ratio = args[3]
step = args[4]
last_idx = len(g_i.vertex_name) - 1
res = nibble(g_i, last_idx, b, phi)
res = res[res != last_idx]
if (len(res) != 0):
res_label = 1 - g_i.get_labels(res)
res_multi = g_i.get_multi_item_labels(res)
res_h = g_i.h_mat[res, :].sign()
res_r = g_i.r_mat[res, :].sign()
#pred = (res_label.sum()*0.5 +
# (g_i.r_mat[res,:].dot(g_i.h_mat[last_idx, :].T) > 0).sum()*0.5)/res.shape[0]
a = res_label.dot(1 - res_multi) / len(res)
c = res_label.dot(res_multi) / len(res)
pred = ((1 - a) * ratio +
(1 - c) - np.sqrt((1 - a)**2 * ratio**2 + (1 - c)**2 + 2 *
(a * c + (a + c) - 1) * ratio)) / (2 * ratio)
# TODO: this is to try a different prediction method
# pred_prod = (res_h.T.dot(1-res_label))/(res_h.sum(0).A1)
with np.errstate(divide='ignore', invalid='ignore'):
pred_prod = (res_r.T.dot(1 - res_label)) / (res_h.T.dot(1 -
res_label))
idx = np.isnan(pred_prod)
pred_prod[idx] = g_i.wgt[idx]
else:
pred = np.nan
pred_prod = g_i.h_mat[:last_idx, :].sum(1)
if g_i.mult_item_label[last_idx]:
pred = pred * ratio
lbl = g_i.get_label(last_idx)
prd_order = (g_i.h_mat[last_idx, :] > 0).todense().A1
lbl_prod = np.sign(g_i.r_mat[last_idx, :].todense().A1)
lbl_prod = lbl_prod[prd_order]
mul_raise_rate = 1.0
num_known = False
if step == 1:
pred = pred * 0 + 1.
res_h1 = (g_i.h_mat[last_idx, :] > 1).todense().A1 * mul_raise_rate + 1
pred_prod = pred_prod * res_h1
pred_prod = pred_prod[prd_order] * pred
# print("Number assumption: %r; Raise multi item return rate by %f times" %
# (num_known, mul_raise_rate))
if num_known:
num_ret = sum(lbl_prod)
if num_ret > 0:
ind = np.argpartition(pred_prod, -num_ret)[-num_ret:]
pred_prod = pred_prod * 0
pred_prod[ind] = 1
else:
pred_prod = pred_prod * 0
return pred, lbl, pred_prod, lbl_prod
def path_metric_approx(last_pm, llr, ui):
if ui == int(0.5 * (1 - np.sign(llr))):
return last_pm
return last_pm + np.abs(llr)
def _sign_flip(A):
"""
Flip the signs of A so that largest absolute value is positive.
"""
signs = np.sign(A[np.argmax(np.abs(A), axis=0), list(range(A.shape[1]))])
return signs * A