np.where(time <= time[frame])[0][-1]是什么意思

这段代码是在使用numpy库中的where函数，用于查找时间序列中小于或等于某个特定帧的最后一个时间点的索引。其中，time是时间序列数组，frame是特定的帧数，np.where(time <= time[frame])是一个布尔数组，用于筛选出小于等于该帧时间点的所有时间点。最后，[0][-1]用于获取筛选结果中的最后一个索引值，即小于等于该帧时间点的最后一个时间点的索引。

相关问题

def update(frame): # 根据时间选择点的位置 # index = np.where(time <= time[frame])[0][-1] # sc.set_offsets([x[index], y[index]]) x=data.loc[data['label']==frame]['position_x'] y=data.loc[data['label']==frame]['position_y'] offsets = np.column_stack((x, y)) sc.set_offsets(offsets) return sc, # 创建动画 ani = FuncAnimation(fig, update, frames=100, interval=20, blit=True) 这段代码为什么这么慢，怎么优化

这段代码执行慢可能是因为数据量较大，每帧都需要重新计算点的位置。可以考虑将数据预处理，把每个时间点对应的位置提前计算好，然后在动画过程中直接调用预处理好的结果即可。这样可以减少计算量，提高代码执行效率。

另外，可以考虑使用缓存来存储计算结果，避免重复计算，提高性能。具体实现方式可以使用 Python 的装饰器来实现。例如，使用 `functools.lru_cache` 装饰器来缓存函数的结果：

import functools

@functools.lru_cache(maxsize=None)
def calculate_positions(label):
    x = data.loc[data['label']==label]['position_x']
    y = data.loc[data['label']==label]['position_y']
    return np.column_stack((x, y))

然后在 `update` 函数中调用 `calculate_positions` 函数来获取点的位置：

def update(frame):
    offsets = calculate_positions(frame)
    sc.set_offsets(offsets)
    return sc,

这样可以大大提高代码执行效率。

base_efron <- function(y_test, y_test_pred) { time = y_test[,1] event = y_test[,2] y_pred = y_test_pred n = length(time) sort_index = order(time, decreasing = F) time = time[sort_index] event = event[sort_index] y_pred = y_pred[sort_index] time_event = time * event unique_ftime = unique(time[event!=0]) m = length(unique_ftime) tie_count = as.numeric(table(time[event!=0])) ind_matrix = matrix(rep(time, times = length(time)), ncol = length(time)) - t(matrix(rep(time, times = length(time)), ncol = length(time))) ind_matrix = (ind_matrix == 0) ind_matrix[ind_matrix == TRUE] = 1 time_count = as.numeric(cumsum(table(time))) ind_matrix = ind_matrix[time_count,] tie_haz = exp(y_pred) * event tie_haz = ind_matrix %*% matrix(tie_haz, ncol = 1) event_index = which(tie_haz!=0) tie_haz = tie_haz[event_index,] cum_haz = (ind_matrix %*% matrix(exp(y_pred), ncol = 1)) cum_haz = rev(cumsum(rev(cum_haz))) cum_haz = cum_haz[event_index] base_haz = c() j = 1 while(j < m+1) { l = tie_count[j] J = seq(from = 0, to = l-1, length.out = l)/l Dm = cum_haz[j] - J*tie_haz[j] Dm = 1/Dm Dm = sum(Dm) base_haz = c(base_haz, Dm) j = j+1 } base_haz = cumsum(base_haz) base_haz_all = unlist( sapply(time, function(x){ if else( sum(unique_ftime <= x) == 0, 0, base_haz[ unique_ftime==max(unique_ftime[which(unique_ftime <= x)])])}), use.names = F) if (length(base_haz_all) < length(time)) { base_haz_all <- c(rep(0, length(time) - length(base_haz_all)), base_haz_all) } return(list(cumhazard = unique(data.frame(hazard=base_haz_all, time = time)), survival = unique(data.frame(surv=exp(-base_haz_all), time = time)))) }改成python代码

def base_efron(y_test, y_test_pred):
time = y_test[:, 0]
event = y_test[:, 1]
y_pred = y_test_pred

n = len(time)
sort_index = np.argsort(time)
time = time[sort_index]
event = event[sort_index]
y_pred = y_pred[sort_index]

time_event = time * event
unique_ftime = np.unique(time[event != 0])
m = len(unique_ftime)

tie_count = np.asarray(np.histogram(time[event != 0])[0])

ind_matrix = np.tile(time, (n, 1)) - np.tile(time, (n, 1)).T
ind_matrix = (ind_matrix == 0).astype(int)
time_count = np.cumsum(np.bincount(time.astype(int)))
ind_matrix = ind_matrix[time_count, :]

tie_haz = np.exp(y_pred) * event
tie_haz = ind_matrix @ tie_haz
event_index = np.where(tie_haz != 0)[0]
tie_haz = tie_haz[event_index]

cum_haz = ind_matrix @ np.exp(y_pred)
cum_haz = np.flip(np.cumsum(np.flip(cum_haz)))
cum_haz = cum_haz[event_index]

base_haz = []
j = 0
while j < m:
l = tie_count[j]
J = np.linspace(0, l-1, l) / l
Dm = cum_haz[j] - J * tie_haz[j]
Dm = 1 / Dm
Dm = np.sum(Dm)

base_haz.append(Dm)
j += 1

base_haz = np.cumsum(base_haz)

base_haz_all = np.zeros_like(time)
for i, x in enumerate(time):
if np.sum(unique_ftime <= x) == 0:
base_haz_all[i] = 0
else:
base_haz_all[i] = base_haz[np.max(np.where(unique_ftime <= x))]

return {'cumhazard': np.unique(np.column_stack((base_haz_all, time)), axis=0),
'survival': np.unique(np.column_stack((np.exp(-base_haz_all), time)), axis=0)}