Calculate the sum of all columns in a2D NumPy array Write a program to calculate the sum of all columns.The size of array is 2*2 Sample data: [[1.2,2.3],[3.4,4.5]] Sample output: 4.66.8

Sure, here's the code to calculate the sum of all columns in a 2D NumPy array:

import numpy as np

# Sample data
arr = np.array([[1.2, 2.3], [3.4, 4.5]])

# Calculate the sum of all columns
column_sums = np.sum(arr, axis=0)

# Print the sums
print(column_sums)

When you run this code with the sample data, you should get the following output:

[4.6 6.8]

This means that the sum of the first column is 4.6, and the sum of the second column is 6.8.

I hope this helps! Let me know if you have any more questions.

相关问题

Please write a code in Python to calculate the composition of fuzzy matrix

Sure, here's an example Python code to calculate the composition of fuzzy matrix:

import numpy as np

# Define two fuzzy matrices A and B
A = np.array([[0.8, 0.2],
              [0.4, 0.6]])

B = np.array([[0.7, 0.3],
              [0.5, 0.5]])

# Define a function to calculate the composition of two fuzzy matrices
def fuzzy_composition(A, B):
    C = np.zeros_like(A)
    for i in range(A.shape[0]):
        for j in range(B.shape[1]):
            max_val = 0
            for k in range(A.shape[1]):
                val = min(A[i,k], B[k,j])
                if val > max_val:
                    max_val = val
            C[i,j] = max_val
    return C

# Calculate the composition of matrices A and B
C = fuzzy_composition(A, B)

print("Matrix A:")
print(A)
print("Matrix B:")
print(B)
print("Composition of A and B:")
print(C)

In this example, we define two fuzzy matrices `A` and `B` using NumPy arrays. We then define a function `fuzzy_composition` that takes two matrices as input and returns their composition. The function iterates over the rows and columns of the output matrix `C`, and for each element it calculates the maximum value of the minimum values of the corresponding row of `A` and column of `B`. Finally, we call the function with matrices `A` and `B` as input and print the result.

import pandas as pd import numpy as np from sklearn.cluster import DBSCAN from sklearn import metrics from sklearn.cluster import KMeans import os def dbscan(input_file): ## 纬度在前，经度在后 [latitude, longitude] columns = ['lat', 'lon'] in_df = pd.read_csv(input_file, sep=',', header=None, names=columns) # represent GPS points as (lat, lon) coords = in_df.as_matrix(columns=['lat', 'lon']) # earth's radius in km kms_per_radian = 6371.0086 # define epsilon as 0.5 kilometers, converted to radians for use by haversine # This uses the 'haversine' formula to calculate the great-circle distance between two points # that is, the shortest distance over the earth's surface # http://www.movable-type.co.uk/scripts/latlong.html epsilon = 0.5 / kms_per_radian # radians() Convert angles from degrees to radians db = DBSCAN(eps=epsilon, min_samples=15, algorithm='ball_tree', metric='haversine').fit(np.radians(coords)) cluster_labels = db.labels_ # get the number of clusters (ignore noisy samples which are given the label -1) num_clusters = len(set(cluster_labels) - set([-1])) print('Clustered ' + str(len(in_df)) + ' points to ' + str(num_clusters) + ' clusters') # turn the clusters in to a pandas series # clusters = pd.Series([coords[cluster_labels == n] for n in range(num_clusters)]) # print(clusters) kmeans = KMeans(n_clusters=1, n_init=1, max_iter=20, random_state=20) for n in range(num_clusters): # print('Cluster ', n, ' all samples:') one_cluster = coords[cluster_labels == n] # print(one_cluster[:1]) # clist = one_cluster.tolist() # print(clist[0]) kk = kmeans.fit(one_cluster) print(kk.cluster_centers_) def main(): path = './datas' filelist = os.listdir(path) for f in filelist: datafile = os.path.join(path, f) print(datafile) dbscan(datafile) if __name__ == '__main__': main()

这是一个 Python 代码，主要使用了 Pandas、NumPy、sklearn.cluster 等库实现了 DBSCAN 和 KMeans 聚类算法。代码读入了一个文件夹中的多个文件，每个文件都是 GPS 坐标点的经纬度信息，然后使用 DBSCAN 算法进行聚类，并且输出了聚类的个数。接着使用 KMeans 算法对每个聚类进行细分，最后输出每个聚类的中心点坐标。