Hướng dẫn python file line number - số dòng tệp python

Làm thế nào để in từ điển python đẹp từ dòng lệnh?

Làm thế nào để in một dòng trống trong C#?

Làm thế nào để in ma trận mà không có số dòng trong r?

Làm thế nào để in từ điển python đẹp từ dòng lệnh?

Làm thế nào để in một dòng trống trong C#?

Làm thế nào để in ma trận mà không có số dòng trong r?

Làm thế nào chúng ta có thể kết hợp nhiều câu lệnh in trên mỗi dòng trong Python?go to the next line automatically. 
 

Cách chụp nhiều trận đấu trong cùng một dòng trong Java Regex

Python3

for line_number, line in enumerate(textfile):
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(line_number,x,y,"Error")
        else:
            print(line_number,x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

for line_number, line in enumerate(textfile):
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(line_number,x,y,"Error")
        else:
            print(line_number,x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

Sẽ dẫn đến điều này: & nbsp;

for line_number, line in enumerate(textfile):
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(line_number,x,y,"Error")
        else:
            print(line_number,x,y,"No error")

Nhưng đôi khi điều đó có thể xảy ra khi chúng tôi không muốn đi đến dòng tiếp theo nhưng muốn in trên cùng một dòng. Vì vậy, những gì chúng ta có thể làm? & Nbsp; & nbsp;
 

Ví dụ: & nbsp; 

for line_number, line in enumerate(textfile):
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(line_number,x,y,"Error")
        else:
            print(line_number,x,y,"No error")

Giải pháp được thảo luận ở đây hoàn toàn phụ thuộc vào phiên bản Python bạn đang sử dụng. & NBSP; & NBSP;
 

In mà không có dòng mới trong Python 2.x

Python

for line_number, line in enumerate(textfile):
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(line_number,x,y,"Error")
        else:
            print(line_number,x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

for line_number, line in enumerate(textfile):
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(line_number,x,y,"Error")
        else:
            print(line_number,x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

for i in range(5):
   print(i)

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

for i in range(5):
   print(i)

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

for i in range(5):
   print(i)

for i in range(5):
   print(i)

for i in range(5):
   print(i)

for i in range(5):
   print(i)

for i in range(5):
   print(i)

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

0
1
2
3
4

for line_number, line in enumerate(textfile):
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(line_number,x,y,"Error")
        else:
            print(line_number,x,y,"No error")

0
1
2
3
4

Output: 

for line_number, line in enumerate(textfile):
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(line_number,x,y,"Error")
        else:
            print(line_number,x,y,"No error")

In mà không có dòng mới trong Python 3.x

python3

for line_number, line in enumerate(textfile):
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(line_number,x,y,"Error")
        else:
            print(line_number,x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

0
1
2
3
4

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

print(“…..” , end=” “)

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

for line_number, line in enumerate(textfile):
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(line_number,x,y,"Error")
        else:
            print(line_number,x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

for i in range(5):
   print(i)

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

for i in range(5):
   print(i)

from scipy.stats import multivariate_normal as mvn

# covariance matrix
sigma = np.array([[2.3, 0, 0, 0],
           [0, 1.5, 0, 0],
           [0, 0, 1.7, 0],
           [0, 0,   0, 2]
          ])
# mean vector
mu = np.array([2,3,8,10])

# input
x1 = np.array([2.1, 3.5, 8., 9.5])
x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]])


def multivariate_normal_pdf(x, mu, cov):
    x_m = x - mu

    if x.ndim > 1:
        sum_ax = 1
        t_ax = [0] 
        t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0
    else:
        sum_ax = 0
        t_ax = range(x_m.ndim)[::-1]


    x_m_t = np.transpose(x_m, axes=t_ax) 
    A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )
    B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax)
    return A * np.exp(B)

print(mvn.pdf(x1, mu, sigma))
print(multivariate_normal_pdf(x1, mu, sigma))

print(mvn.pdf(x2, mu, sigma))
print(multivariate_normal_pdf(x2, mu, sigma))

for i in range(5):
   print(i)

for i in range(5):
   print(i)

for i in range(5):
   print(i)

for i in range(5):
   print(i)

for i in range(5):
   print(i)

file_name = "whatever.txt"
textfile = open(file_name, "r")
line_number = 0 # this integer will store the line number
for line in (textfile):
    line_number += 1
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(x,y,"Error")
        else:
            print(x,y,"No error")

In mà không có dòng mới trong Python 3.x

Output: 

for line_number, line in enumerate(textfile):
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(line_number,x,y,"Error")
        else:
            print(line_number,x,y,"No error")

0 1 2 3 43for line_number, line in enumerate(textfile): for x, y in enumerate(zip(line.split(), line.split()[1:])): if(x,y)==(y,x): print(line_number,x,y,"Error") else: print(line_number,x,y,"No error") 90 1 2 3 47from scipy.stats import multivariate_normal as mvn # covariance matrix sigma = np.array([[2.3, 0, 0, 0], [0, 1.5, 0, 0], [0, 0, 1.7, 0], [0, 0, 0, 2] ]) # mean vector mu = np.array([2,3,8,10]) # input x1 = np.array([2.1, 3.5, 8., 9.5]) x2 = np.array([[2.1, 3.5, 8., 9.5],[2.2, 3.6, 8.1, 9.6]]) def multivariate_normal_pdf(x, mu, cov): x_m = x - mu if x.ndim > 1: sum_ax = 1 t_ax = [0] t_ax.extend(list(range(x_m.ndim)[:0:-1])) # transpose dims > 0 else: sum_ax = 0 t_ax = range(x_m.ndim)[::-1] x_m_t = np.transpose(x_m, axes=t_ax) A = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) ) B = (-1/2) * np.sum(x_m_t.dot(np.linalg.inv(cov)) * x_m,axis=sum_ax) return A * np.exp(B) print(mvn.pdf(x1, mu, sigma)) print(multivariate_normal_pdf(x1, mu, sigma)) print(mvn.pdf(x2, mu, sigma)) print(multivariate_normal_pdf(x2, mu, sigma)) 6print(“…..” , end=” “)1file_name = "whatever.txt" textfile = open(file_name, "r") line_number = 0 # this integer will store the line number for line in (textfile): line_number += 1 for x, y in enumerate(zip(line.split(), line.split()[1:])): if(x,y)==(y,x): print(x,y,"Error") else: print(x,y,"No error") 2

Python3

Output:

for line_number, line in enumerate(textfile):
    for x, y in enumerate(zip(line.split(), line.split()[1:])):
        if(x,y)==(y,x):
            print(line_number,x,y,"Error")
        else:
            print(line_number,x,y,"No error")