我正在尝试格式化许多电影院的电影列表。大多数影片放映许多相同的电影,但时间不同。我正在使用.csv。
以下是导入并创建列表作为字典后的示例:
def my_decision_stump(X, D, y):
Fs = np.inf
optimal_j = None
optimal_b = None
optimal_theta = None
m, d = X.shape
for j in range(d):
record = np.hstack((X[:, j].reshape(-1, 1), D.reshape(-1, 1), y.reshape(-1, 1)))
record_sorted = record[record[:, 0].argsort()]
x = record_sorted[:, 0]; D = record_sorted[:, 1]; y = record_sorted[:, 2]
x = np.hstack((x, x[-1]+1))
F_pos = np.sum(D[y == 1])
F_neg = np.sum(D[y == -1])
if F_pos < Fs or F_neg < Fs:
optimal_theta = x[0] - 1; optimal_j = j
if F_pos < F_neg:
Fs = F_pos; optimal_b = 1
else:
Fs = F_neg; optimal_b = -1
for i in range(m):
F_pos -= y[i] * D[i]
F_neg += y[i] * D[i]
if (F_pos < Fs or F_neg < Fs) and x[i] != x[i+1]:
optimal_theta = 0.5 * (x[i] + x[i+1]); optimal_j = j
if F_pos < F_neg:
Fs = F_pos; optimal_b = 1
else:
Fs = F_neg; optimal_b = -1
return (optimal_j, optimal_b, optimal_theta)
def decision_stump(X, D, y):
Fs = np.inf
optimal_j = None
optimal_b = None
optimal_theta = None
m, d = X.shape
for j in range(d):
index = np.argsort(X[:, j])
x = np.zeros(m+1)
x[:-1] = X[index, j]
x[-1] = x[-2] + 1
F_pos = np.sum(D[y == 1])
F_neg = np.sum(D[y == -1])
if F_pos < Fs or F_neg < Fs:
optimal_theta = x[0] - 1; optimal_j = j
if F_pos < F_neg:
Fs = F_pos; optimal_b = 1
else:
Fs = F_neg; optimal_b = -1
for i in range(m):
curr_idx = index[i]
F_pos -= y[curr_idx] * D[curr_idx]
F_neg += y[curr_idx] * D[curr_idx]
if (F_pos < Fs or F_neg < Fs) and x[i] != x[i+1]:
optimal_theta = 0.5 * (x[i] + x[i+1]); optimal_j = j
if F_pos < F_neg:
Fs = F_pos; optimal_b = 1
else:
Fs = F_neg; optimal_b = -1
return (optimal_j, optimal_b, optimal_theta)
并且我正在寻找这样的输出:
holygrail : {
rating : pg,
cinema : regal,
times : 1, 3, 5
}
holygrail : {
rating : pg,
cinema : amc,
times : 2, 4, 6
}
或
holygrail : {
rating: pg,
cinema1 : {cinema : regal, times : 1, 3, 5},
cinema2 : {cinema : amc, times : 2, 4, 6}
}
在我看来,我可以制作电影类的实例,但不确定如何将其整合到字典中。