You can download Skandalakisanatomiaquirurgicapdf22 from these fast and secure servers:Examination of the first phase of the human placenta. Effect of intravenous dipyrone on the time course of the first and second stages of labour.
The effects of dipyrone given by intravenous injection on the course of labour were investigated in 22 parturients, using an equipotent dose of 10 mg/kg. Serial assessment of the strength of uterine contractions demonstrated that there was a significant reduction of the duration of the first (2.1 +/- 1.4 hours) and second stage (1.9 +/- 1.3 hours) of labour. As the differences were not statistically significant, there appears to be little apparent clinical significance associated with this drug, and most of the babies in this study are unlikely to have suffered clinical effects.Q:
Which algorithm should I use to find the similarities between two sets of values?
Sorry if my question sounds stupid, but I am learning by the internet.
I have two arrays of real numbers:
a = np.array([ 1.2, 2.4, 3.2, 4.4, 5.2, 7.4, 8.4, 11.3, 12.3, 13.2, 14.1])
b = np.array([ 2.3, 3.4, 6.3, 7.3, 8.4, 11.5, 12.2, 13.3, 14.1, 14.5])
I want to check if the elements in the array a are similar to the elements in the array b. To that end, I have used np.percentile to calculate the similarity:
c = np.percentile(a,10)
d = np.percentile(b,10)
s = (c==d)
s = (c==d)
So, this line creates a boolean matrix (1st and 3rd column are identical) where 1 means the percentages in a and b are similar, and 0 means they are not.
Now, I want to calculate the average of the percentage of matching elements between all similar arrays.
array( [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1])
Which should mean:
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