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我试图用PCA降低数据集的维数。然后，根据某些标准（取决于从中获取数据点的文件名的编号），为每个数据点分配一个“类/类别”，并将所有数据点绘制为散点图，其中包含遗留的有关散点图的其他信息的问题
如同每个数据点的另一个列表我有一些附加信息存储，我希望每个数据点都可以选择，以便我可以读取终端中的信息。 在绘制我的散点图时 - 我假设因为我绘制了子集明智的 - 订单被搞乱了。 接收到的事件的标记不再适用于具有附加信息的阵列。
我试图在绘图时重新排列信息数组，但不知何故它仍然无法工作。这里是我的代码：
targets = []
trainNames = []
# React on to a click on a datapoint.
def onPick(event):
indexes = event.ind
xy = event.artist.get_offsets()
for index in indexes:
print trainNames[index]
# Load the additonal information for each datapoint. It's stored in the
# same order as the datapoints in 'trainingfile.csv'.
modelNamesFile = open("training-names.csv")
for line in modelNamesFile:
# Save target for datapoint. It's the class of the object, seperated
# into "rectangular", "cylindrical", "irregular", dependend on the
# objects file number.
objnum = int(line.split(",")[-1].split("/")[-1].split(".")[0])
if (objnum <= 16):
objnum = 0
elif (objnum >= 17 and objnum <= 34):
objnum = 1
else:
objnum = 2
targets.append(objnum)
# Save name description for datapoint.
sceneName = line.split(",")[0].split("/")[-1]
modelName = line.split(",")[-1].split("/")[-1].split(".")[0]
trainNames.append(sceneName + ", " + modelName)
target_names = ["rectangular", "cylindrical", "irregular"]
# Load the actual data.
f = open("trainingfile.csv")
tData = []
for line in f:
lsplit = line.split(",")
datapoint = []
for feature in lsplit:
datapoint.append(float(feature))
tData.append(datapoint)
data = np.array(tData)
# Transform it into 2D with PCA.
y = np.array(targets)
X = np.delete(data, data.shape[1] - 1, 1) # Strip class.
pipeline = Pipeline([('scaling', StandardScaler()), ('pca', PCA(n_components=2))])
X_reduced = pipeline.fit_transform(data)
# Create plot.
trainNames = np.array(trainNames)
tmpTrainNames = np.array([])
fig = plt.figure()
for c, i, target_name in zip("rgb", [0, 1, 2], target_names):
plt.scatter(X_reduced[y == i, 0], X_reduced[y == i, 1], c=c, label=target_name, picker=True)
# Here i try to rearrange the order of the additonal information int he order the points
# were plotted.
tmpTrainNames = np.append(tmpTrainNames, trainNames[y == i])
trainNames = tmpTrainNames
plt.legend()
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
fig.canvas.mpl_connect('pick_event', onPick)
plt.show()
如果它太复杂，我可以尝试简化。就告诉我嘛。