在数组中寻找最小值 [英] Finding minimum in an array

问题描述

我想在idx 2-7之间找到最小的"y"数字，但是我做错了什么. 目前它打印x = 0.02和y = 101，我希望它打印出x = 0.05和y = 104. 即使我将"idx = 3"更改为更高的数字，也不会更改.

i want to find the smallest "y" number between idx 2-7, but theres something im not doing right. For the moment it prints x = 0.02 and y = 101, i want it to print out x = 0.05 and y = 104. Even if i change the "idx = 3" to a higher number nothing changes.

我已将其从max更改为min，因此仍然有人说max，但是只要"y [:idx] .argmin()"为min，我认为这并不重要吗?

I have changed it from max to min, therefor some still say max, but i dont think that matters as long as " y[:idx].argmin()" is min?

import numpy as np
# idx:           0     1     2     3     4     5     6     7
x = np.array([0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08]) # strain
y = np.array([ 110,  101,  110,  106,  102,  104,  112,  115]) # load


idx = 3
cutoff = 0.08
while x[idx] < cutoff:
    idx = idx + 1

max_idx = y[:idx].argmin()
max_x = x[max_idx]
max_y = y[max_idx]
print (max_x)
print (max_y)

推荐答案

y[:idx]是第一个idx值.您要y[2:].

此外，min_idx = y[2:].argmin()还提供关于y[2:]的最小索引. 因此，相对于y的最小索引应为2+min_idx.

Also, min_idx = y[2:].argmin() gives you the min index with respect to y[2:]. So the min index with respect to y would be 2+min_idx.

import numpy as np
# idx:           0     1     2     3     4     5     6     7
x = np.array([0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08]) # strain
y = np.array([ 110,  101,  110,  106,  102,  104,  112,  115]) # load

min_idx = y[2:].argmin()
min_x = x[2+min_idx]
min_y = y[2+min_idx]
print (min_x)
# 0.05

print (min_y)
# 102

---

如果您希望将注意力集中在x> = 0.03并且x <0.07，然后使用布尔掩码将x和y限制为这些值:

---

If you wish to restrict attention to those values of x for which x >= 0.03 and x < 0.07, then use a boolean mask to restrict x and y to those values:

import numpy as np
# idx:           0     1     2     3     4     5     6     7
x = np.array([0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08]) # strain
y = np.array([ 110,  101,  110,  106,  102,  104,  112,  115]) # load

lower, upper = 0.03, 0.07
mask = (x >= lower) & (x < 0.07)
# array([False, False,  True,  True,  True,  True, False, False], dtype=bool)

# select those values of x and y
masked_y = y[mask]
masked_x = x[mask]

# find the min index with respect to masked_y
min_idx = masked_y.argmin()

# find the values of x and y restricted to the mask, having the min y value
min_x = masked_x[min_idx]
min_y = masked_y[min_idx]

print (min_x)
# 0.05

print (min_y)
# 102

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