def iu_status_filter(i, data, num): val_last = data.iloc[i-1]['iu34Status01'] val_now = data.iloc[i]['iu34Status01'] if val_last == 0 and val_now > 0: if (data.iloc[i:i+num]['iu34Status01'] == 2).all(): return True elif (data.iloc[i:i+num]['iu34Status01'] == 1).all(): return True else: # prev_val = data.iloc[i]['iu34Status01'] for index in range(i+1, i+num): curr_val = data.iloc[index]['iu34Status01'] if curr_val != val_now and curr_val <= val_now: return False val_now = curr_val return True else: return False

这是一个函数，可以根据给定的数据和数字，过滤出符合一定条件的数据。具体来说，当给定数据的第i行的iu34Status01值从0变为非0时，会检查接下来的num行数据的iu34Status01值是否都为2或都为1，如果都是，则返回True；否则，会检查接下来的num行数据的iu34Status01值是否都等于当前值或者更大，如果是，则返回True；否则，返回False。

相关问题

def train(self, train_data): _. y_train, features = train_ data. iloc[:, :-1], train data. iloc[:,-1], train data.colums[:-1]

# Splitting the data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(features, y_train, test_size=0.2, random_state=42)

# Training the model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Evaluating the model on the validation set
y_pred = model.predict(X_val)
accuracy = accuracy_score(y_val, y_pred)

print("Accuracy:", accuracy)

class Queue: def __init__(self, limit=10): self.data = [None] * limit self.head = -1 self.tail = -1

def is_empty(self): return self.head == -1 def is_full(self): return (self.tail + 1) % len(self.data) == self.head def enqueue(self, val): if self.is_full(): raise Exception("Queue is full") if self.head == -1: self.head = 0 self.tail = (self.tail + 1) % len(self.data) self.data[self.tail] = val def dequeue(self): if self.is_empty(): raise Exception("Queue is empty") val = self.data[self.head] if self.head == self.tail: self.head = -1 self.tail = -1 else: self.head = (self.head + 1) % len(self.data) return val