def es_predict_sum(count_list, virtual_info, double_or_triple, alpha):
# temp_list = []
es_predict_result = {}
for flavor, info in virtual_info.items():
temp_list1 = []
temp_list2 = []
temp_list3 = []
for i in range(len(count_list)):
temp_list1.append(count_list[i][flavor - 1])
temp_a = Tool.mid([count_list[j][flavor - 1] for j in range(i - 2, i + 2) if 0 <= j < len(count_list)])
temp_list2.append(
math.log(
int(temp_a) + 1))
temp_list3.append(count_list[i][flavor - 1])
temp_list1 = temp_list1[::-1]
temp_list2 = temp_list2[::-1]
es_list3 = Tool.line_diff(temp_list3[::-1])
if double_or_triple == 2:
a1, b1 = compute_double(alpha, temp_list1)
result1 = max(int(a1[-1] + b1[-1] * 1), 0)
a2, b2 = compute_double(alpha, temp_list2)
result2 = max(math.exp(a2[-1] + b2[-1] * 1) - 1, 0)
a3, b3 = compute_double(alpha, es_list3)
aaa = int(a3[-1] + b3[-1] * 1)
result3 = max(temp_list3[0] + aaa, 0)
es_predict_result[flavor] = max(0.33 * result1 + 0.33 * result2 + 0.33 * result3, 0)
elif double_or_triple == 3:
a1, b1, c1 = compute_triple(alpha, temp_list1)
result1 = max(int(a1[-1] + b1[-1] * 1 + c1[-1] * (1 ** 2)), 0)
a2, b2, c2 = compute_triple(alpha, temp_list2)
result2 = max(math.exp(a2[-1] + b2[-1] * 1 + c2[-1] * (1 ** 2)) - 1, 0)
a3, b3, c3 = compute_triple(alpha, es_list3)
bbb = int(a3[-1] + b3[-1] * 1 + c3[-1] * (1 ** 2))
result3 = max(temp_list3[0] + bbb, 0)
es_predict_result[flavor] = max(0.33 * result1 + 0.33 * result2 + 0.33 * result3, 0)
return es_predict_result
def es_predict_log(count_list, virtual_info, double_or_triple, alpha):
# temp_list = []
es_predict_result = {}
for flavor, info in virtual_info.items():
temp_list = []
for i in range(len(count_list)):
# temp_a = count_list[i][flavor - 1]
temp_a = Tool.mid([count_list[j][flavor - 1] for j in range(i - 2, i + 2) if 0 <= j < len(count_list)])
temp_list.append(
math.log(
int(temp_a) + 1))
temp_list = temp_list[::-1]
if double_or_triple == 2:
a, b = compute_double(alpha, temp_list)
es_predict_result[flavor] = max(math.exp(a[-1] + b[-1] * 1) - 1, 0)
elif double_or_triple == 3:
a, b, c = compute_triple(alpha, temp_list)
es_predict_result[flavor] = max(math.exp(a[-1] + b[-1] * 1 + c[-1] * (1 ** 2)) - 1, 0)
return es_predict_result
def predict(lg_count_list,
lr_count_list,
rf_count_list,
es_count_list,
virtual_info,
lg_round,
mix_rf,
mix_lr,
mix_es,
rf_day_gap,
lr_day_gap,
es=3,
alpha=0.5,
seed=1000,
floor=0.0,
rf_diff=0):
print '-----start predict-----'
lg_predict_result = {}
rf_predict_result = {}
lr_predict_result = {}
es_predict_result = {}
for flavor, info in virtual_info.items():
# ----------- 拉格朗日 ----------
lg_window = []
for i in range(lg_round):
lg_window.append(
# Tool.mid([lg_count_list[j][flavor - 1] for j in range(i - 7, i + 7) if 0 <= j < lg_round]))
Tool.mid([
lg_count_list[j][flavor - 1] for j in range(i - 3, i + 3)
if 0 <= j < lg_round
]))
# window_list = [Tool.mean(lg_window[15:29]), Tool.mean(lg_window[0:29]), Tool.mean(lg_window[0:14])]
window_list = [
Tool.mean(lg_window[4:7]),
Tool.mean(lg_window[0:7]),
Tool.mean(lg_window[0:3])
]
lg_predict_result[flavor] = max(
int(Tool.LG(3, list(range(0, 3)), window_list)), 0)
# ----------- 随机森林 ----------
rf_predict_list = []
temp_list = []
for i in range(len(rf_count_list)):
# temp_list.append(Tool.mid([rf_count_list[j][flavor - 1] for j in range(i - 4, i + 4) if 0 <= j < len(rf_count_list)]))
temp_list.append(rf_count_list[i][flavor - 1])
test = temp_list[:rf_day_gap - 1][::-1]
diff_list = Tool.line_diff(temp_list)
if rf_diff == 0:
for j in range(len(rf_count_list) - rf_day_gap):
rf_predict_list.append(temp_list[j:j + rf_day_gap][::-1])
my_labels = [i for i in range(rf_day_gap)]
rf_result = rf_predict(rf_predict_list, my_labels, test, seed)
rf_predict_result[flavor] = rf_result
else:
for j in range(len(rf_count_list) - rf_day_gap):
rf_predict_list.append(diff_list[j:j + rf_day_gap][::-1])
my_labels = [i for i in range(rf_day_gap)]
rf_result = rf_predict(rf_predict_list, my_labels, test, seed)
rf_predict_result[flavor] = rf_result + temp_list[-1]
# ----------- 线性回归 ----------
lr_window = []
lr_data = []
for i in range(20 - lr_day_gap + 1):
for j in range(1, lr_day_gap):
# lr_window.append(lr_count_list[i + j][int(flavor) - 1])
lr_window.append(
Tool.mid([
lr_count_list[i + jj][int(flavor) - 1]
for jj in range(j - 3, j + 3) if 0 <= jj < lr_day_gap
]))
# lr_window.append(Tool.mid([lr_count_list[i+jj][int(flavor) - 1] for jj in range(j - 3, j + 3) if 0 <= jj < lr_day_gap]))
lr_window.append(lr_count_list[i][int(flavor) - 1])
lr_data.append(lr_window)
lr_window = []
# print lr_data
w = Tool.zeros(lr_day_gap)
w = linear_regression(w, lr_data, 0.02, 700) # 0.03 500
x = [lr_data[0][lr_day_gap - 1]] + lr_data[0][:lr_day_gap - 2]
x = x + [u * u for u in x]
max_x = max(x)
min_x = min(x)
if max_x - min_x > 0:
x = [1] + [(i - (sum(x) / len(x))) / (max_x - min_x) for i in x]
else:
x = [1] + x
# lr_predict_result[flavor] = max(int(LR.w_mul_x(w, x)), 0)
lr_predict_result[flavor] = max(w_mul_x(w, x), 0)
# ---------- 指数平滑 -------------
es_predict_result = es_predict(es_count_list, virtual_info, es, alpha)
print 'LG预测结果：{}'.format(lg_predict_result)
print 'LR预测结果：{}'.format(lr_predict_result)
print 'RF预测结果：{}'.format(rf_predict_result)
print 'ES预测结果：{}'.format(es_predict_result)
predict_result = lg_predict_result.copy()
for key, value in predict_result.items():
# predict_result[key] = int(MIX_NUM*lg_predict_result[key] + (1-MIX_NUM)*lr_predict_result[key]) # 向下取整
predict_result[key] = int(
mix_lr * lr_predict_result[key] + mix_rf * rf_predict_result[key] +
mix_es * es_predict_result[key] +
(1 - mix_lr - mix_rf - mix_es) * lg_predict_result[key] +
floor) # 四舍五入
# predict_result[key] = int(
# mix_lr * lr_predict_result[key] + mix_rf * rf_predict_result[key] + mix_es * es_predict_result[key] +
# (1 - mix_lr - mix_rf - mix_es) * lg_predict_result[key]) # 地板除
print '最终预测结果：{}'.format(predict_result)
print '-----end predict-----'
return predict_result