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This repository has been archived by the owner on Nov 1, 2024. It is now read-only.
Generate data set for training using opengpt. And also create a parser to separate the coded from the context.
The starting points would be filtering techniques: e.g. Kalman Filter and IIR filter.
Convert:
An infinite impulse response (IIR) filter is a type of digital filter that can be used to implement a variety of different frequency response characteristics. It is characterized by its recursive structure, which allows it to have an impulse response that extends indefinitely into the past. Here is some sample code that demonstrates how to implement an IIR filter using a direct form II structure:
import numpy as np
class IIRFilter:
def __init__(self, b, a):
self.b = b
self.a = a
self.z = np.zeros(max(len(b), len(a)))
def filter(self, x):
y = np.zeros_like(x)
for n in range(len(x)):
y[n] = self.b[0] * x[n]
for m in range(1, len(self.b)):
if n-m >= 0:
y[n] += self.b[m] * x[n-m]
for m in range(1, len(self.a)):
if n-m >= 0:
y[n] -= self.a[m] * y[n-m]
self.z[0] = x[n]
self.z[1:] = self.z[:-1]
return y
to
result[0] = "An infinite impulse response (IIR) filter is a type of digital filter that can be used to implement a variety of different frequency response characteristics. It is characterized by its recursive structure, which allows it to have an impulse response that extends indefinitely into the past. Here is some sample code that demonstrates how to implement an IIR filter using a direct form II structure:"
result[1] =
import numpy as np
class IIRFilter:
def __init__(self, b, a):
self.b = b
self.a = a
self.z = np.zeros(max(len(b), len(a)))
def filter(self, x):
y = np.zeros_like(x)
for n in range(len(x)):
y[n] = self.b[0] * x[n]
for m in range(1, len(self.b)):
if n-m >= 0:
y[n] += self.b[m] * x[n-m]
for m in range(1, len(self.a)):
if n-m >= 0:
y[n] -= self.a[m] * y[n-m]
self.z[0] = x[n]
self.z[1:] = self.z[:-1]
return y
The example above is a transformer node.
For this issue, we would like to prioritize the following nodes (priority high to low):
🚀 Feature
Generate data set for training using opengpt. And also create a parser to separate the coded from the context.
The starting points would be filtering techniques: e.g. Kalman Filter and IIR filter.
Convert:
An infinite impulse response (IIR) filter is a type of digital filter that can be used to implement a variety of different frequency response characteristics. It is characterized by its recursive structure, which allows it to have an impulse response that extends indefinitely into the past. Here is some sample code that demonstrates how to implement an IIR filter using a direct form II structure:
to
result[0] = "An infinite impulse response (IIR) filter is a type of digital filter that can be used to implement a variety of different frequency response characteristics. It is characterized by its recursive structure, which allows it to have an impulse response that extends indefinitely into the past. Here is some sample code that demonstrates how to implement an IIR filter using a direct form II structure:"
result[1] =
The example above is a transformer node.
For this issue, we would like to prioritize the following nodes (priority high to low):
Additional context
The text was updated successfully, but these errors were encountered: