本文整理匯總了Python中scipy.signal.html方法的典型用法代碼示例。如果您正苦於以下問題：Python signal.html方法的具體用法？Python signal.html怎麽用？Python signal.html使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類scipy.signal的用法示例。
在下文中一共展示了signal.html方法的7個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: discount_cumsum
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import html [as 別名]
def discount_cumsum(x, discount):
"""Discounted cumulative sum.
See https://docs.scipy.org/doc/scipy/reference/tutorial/signal.html#difference-equation-filtering # noqa: E501
Here, we have y[t] - discount*y[t+1] = x[t]
or rev(y)[t] - discount*rev(y)[t-1] = rev(x)[t]
Args:
x (np.ndarrary): Input.
discount (float): Discount factor.
Returns:
np.ndarrary: Discounted cumulative sum.
"""
return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1],
axis=0)[::-1] 示例2: batch_discounted_cumsum
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import html [as 別名]
def batch_discounted_cumsum(values, discount):
"""
Return a matrix of discounted returns.
output[i, j] = discounted sum of returns of rewards[i, j:]
So
output[i, j] = rewards[i, j] + rewards[i, j+1] * discount
+ rewards[i, j+2] * discount**2 + ...
Based on rllab.misc.special.discounted_cumsum
:param rewards: FloatTensor, size [batch_size, sequence_length, 1]
:param discount: float, discount factor
:return FloatTensor, size [batch_size, sequence_length, 1]
"""
# See https://docs.scipy.org/doc/scipy/reference/tutorial/signal.html#difference-equation-filtering
# Here, we have y[t] - discount*y[t+1] = x[t]
# or reverse(y)[t] - discount*reverse(y)[t-1] = reverse(x)[t]
return scipy.signal.lfilter(
[1], [1, float(-discount)], values.T[::-1], axis=0,
)[::-1].T 示例3: discount_cumsum
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import html [as 別名]
def discount_cumsum(x, discount):
# See https://docs.scipy.org/doc/scipy/reference/tutorial/signal.html#difference-equation-filtering
# Here, we have y[t] - discount*y[t+1] = x[t]
# or rev(y)[t] - discount*rev(y)[t-1] = rev(x)[t]
return scipy.signal.lfilter([1], [1, -discount], x[::-1], axis=0)[::-1] 示例4: discount_cumsum
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import html [as 別名]
def discount_cumsum(x, discount):
# See https://docs.scipy.org/doc/scipy/reference/tutorial/signal.html#difference-equation-filtering # noqa: E501
# Here, we have y[t] - discount*y[t+1] = x[t]
# or rev(y)[t] - discount*rev(y)[t-1] = rev(x)[t]
return scipy.signal.lfilter(
[1], [1, float(-discount)], x[::-1], axis=0)[::-1] 示例5: discount_cumsum
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import html [as 別名]
def discount_cumsum(x, gamma):
"""Compute the discounted cumulative summation of an 1-d array.
From https://github.com/rll/rllab/blob/master/rllab/misc/special.py"""
# See https://docs.scipy.org/doc/scipy/reference/tutorial/signal.html#difference-equation-filtering
# Here, we have y[t] - discount*y[t+1] = x[t]
# or rev(y)[t] - discount*rev(y)[t-1] = rev(x)[t]
return scipy.signal.lfilter([1], [1, float(-gamma)], x[::-1], axis=0)[::-1] 示例6: discount_cumsum
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import html [as 別名]
def discount_cumsum(x, discount):
"""
See https://docs.scipy.org/doc/scipy/reference/tutorial/signal.html#difference-equation-filtering
Returns:
(float) : y[t] - discount*y[t+1] = x[t] or rev(y)[t] - discount*rev(y)[t-1] = rev(x)[t]
"""
return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], axis=0)[::-1] 示例7: discount_cumsum
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import html [as 別名]
def discount_cumsum(x, discount):
# See https://docs.scipy.org/doc/scipy/reference/tutorial/signal.html#difference-equation-filtering
# Here, we have y[t] - discount*y[t+1] = x[t]
# or rev(y)[t] - discount*rev(y)[t-1] = rev(x)[t]
return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], axis=0)[::-1]