I want to get the covariance from the iris data set, https://www.kaggle.com/jchen2186/machine-learning-with-iris-dataset/data
I am using numpy, and the function -> np.cov(iris)
with open("Iris.csv") as iris:
reader = csv.reader(iris)
data = []
next(reader)
for row in reader:
data.append(row)
for i in data:
i.pop(0)
i.pop(4)
iris = np.array(data)
np.cov(iris)
And I get this error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-bfb836354075> in <module>
----> 1 np.cov(iris)
D:\Anaconda\lib\site-packages\numpy\lib\function_base.py in cov(m, y, rowvar, bias, ddof, fweights, aweights)
2300 w *= aweights
2301
-> 2302 avg, w_sum = average(X, axis=1, weights=w, returned=True)
2303 w_sum = w_sum[0]
2304
D:\Anaconda\lib\site-packages\numpy\lib\function_base.py in average(a, axis, weights, returned)
354
355 if weights is None:
--> 356 avg = a.mean(axis)
357 scl = avg.dtype.type(a.size/avg.size)
358 else:
D:\Anaconda\lib\site-packages\numpy\core\_methods.py in _mean(a, axis, dtype, out, keepdims)
73 is_float16_result = True
74
---> 75 ret = umr_sum(arr, axis, dtype, out, keepdims)
76 if isinstance(ret, mu.ndarray):
77 ret = um.true_divide(
TypeError: cannot perform reduce with flexible type
I don't understand what it means..
So, if you want to modify your code you could try by reading the Iris.csv with pandas.read_csv function. And then select the appropiate columns of your choice.
BUT, here is a little set of commands to ease up this task. They use scikit-learn and numpy to load the iris dataset obtain X and y and obtain covariance matrix:
from sklearn.datasets import load_iris
import numpy as np
data = load_iris()
X = data['data']
y = data['target']
np.cov(X)
Hope this has helped.