add: Bind Filter and Fit

This commit is contained in:
liuyihui 2022-07-08 21:21:48 +08:00
parent 3dafde3a67
commit f3931069bd
7 changed files with 105 additions and 36 deletions

1
.gitignore vendored
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@ -11,6 +11,7 @@ venv/
# build cache # build cache
build/ build/
__pycache__
# config # config
.vscode .vscode

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@ -1,36 +0,0 @@
import os
import numpy as np
from tqdm import tqdm
from matplotlib import pyplot as plt
from sklearn.mixture import GaussianMixture
path = 'result/bind'
file_list = os.listdir(path)
pbar = tqdm(total=len(file_list))
for file in file_list:
name = file.split('.')[0]
file = os.path.join(path, file)
data = np.loadtxt(file, dtype=np.uint16)
x = data[:, 0]
y = data[:, 1]
model = GaussianMixture(n_components=2)
model.fit(data)
nx = np.array([])
ny = model.predict(data)
fig = plt.figure(figsize=(8, 8))
for cluster in np.unique(ny):
idx = np.where(ny == cluster)[0]
nx = idx if len(idx) > len(nx) else nx
plt.scatter(data[idx, 0], data[idx, 1], s=0.1)
fig.savefig('result/GMM/' + name + '.png')
plt.close()
np.savetxt('result/bind-GMM/' + name + '.txt', data[nx], fmt='%d')
pbar.update(1)
pbar.close()

30
main.py Normal file
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@ -0,0 +1,30 @@
import os
import re
import numpy as np
from tqdm import tqdm
from qdx import BindFilter, Bind
binds = []
BF = BindFilter()
path = 'result/bind'
file_list = os.listdir(path)
reg = re.compile(r'(([0-9]{4})-([0-9])-([0.9])).txt')
pbar = tqdm(desc="Gaussian Mixture Bind Filter", total=len(file_list))
for file in file_list:
name, E, n, m = reg.match(file).groups()
file = os.path.join(path, file)
BF(file)
BF.draw('result/GMM/' + name + '.png')
np.savetxt('result/bind-GMM/' + name + '.txt', BF.fit_data, fmt='%d')
binds.append(Bind(int(E), int(n), int(m), BF.fit_data))
pbar.update(1)
break
pbar.close()

28
qdx/Bind.py Normal file
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from cProfile import label
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
class Bind(object):
k, b = 0, 0
def __init__(self, E, n, m, data):
self.E = E
self.n, self.m = n, m
self.x, self.y = data[:, 0], data[:, 1]
def fit(self):
self.reg = LinearRegression()
self.reg.fit(self.x, self.y)
self.k = self.reg.coef_[0][0]
self.b = self.reg.intercept_[0]
def draw(self, title):
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(1, 1, 1)
ax.scatter(self.x, self.y, s=0.1, c='k', label='raw')
ax.plot(self.x, self.reg.predict(self.x), c='r', label='fit')
ax.legend()
fig.savefig(title)
plt.close()

33
qdx/BindFilter.py Normal file
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import numpy as np
from matplotlib import pyplot as plt
from sklearn.mixture import GaussianMixture
class BindFilter(object):
def __init__(self):
pass
def fit(self, file):
self.clusters = []
self.fit_data = np.array([])
data = np.loadtxt(file, dtype=np.uint16)
model = GaussianMixture(n_components=2)
model.fit(data)
ny = model.predict(data)
for i in np.unique(ny):
idx = np.where(ny == i)[0]
self.fit_data = idx if len(idx) > len(self.fit_data) else self.fit_data
self.clusters.append(data[idx])
self.fit_data = data[self.fit_data]
def draw(self, title):
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(1, 1, 1)
for cluster in self.clusters:
ax.scatter(cluster[:, 0], cluster[:, 1], s=0.1)
fig.savefig(title)
plt.close()

11
qdx/Block.py Normal file
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from .Bind import Bind
class Block(object):
binds = []
def __init__(self) -> None:
pass
def addBind(self, b: Bind):
self.binds.append(b)

2
qdx/__init__.py Normal file
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@ -0,0 +1,2 @@
from .Bind import Bind
from .BindFilter import BindFilter