numpy.random.uniform#
Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform .
New code should use the uniform method of a Generator instance instead; please see the Quick Start .
Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.
high float or array_like of floats
Upper boundary of the output interval. All values generated will be less than or equal to high. The high limit may be included in the returned array of floats due to floating-point rounding in the equation low + (high-low) * random_sample() . The default value is 1.0.
size int or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k) , then m * n * k samples are drawn. If size is None (default), a single value is returned if low and high are both scalars. Otherwise, np.broadcast(low, high).size samples are drawn.
Returns : out ndarray or scalar
Drawn samples from the parameterized uniform distribution.
Discrete uniform distribution, yielding integers.
Discrete uniform distribution over the closed interval [low, high] .
Floats uniformly distributed over [0, 1) .
Convenience function that accepts dimensions as input, e.g., rand(2,2) would generate a 2-by-2 array of floats, uniformly distributed over [0, 1) .
which should be used for new code.
The probability density function of the uniform distribution is
anywhere within the interval [a, b) , and zero elsewhere.
>>> x = np.float32(5*0.99999999) >>> x 5.0
Draw samples from the distribution:
All values are within the given interval:
>>> np.all(s >= -1) True >>> np.all(s 0) True
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 15, density=True) >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') >>> plt.show()
numpy.random.uniform#
Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform .
New code should use the uniform method of a Generator instance instead; please see the Quick Start .
Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.
high float or array_like of floats
Upper boundary of the output interval. All values generated will be less than or equal to high. The high limit may be included in the returned array of floats due to floating-point rounding in the equation low + (high-low) * random_sample() . The default value is 1.0.
size int or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k) , then m * n * k samples are drawn. If size is None (default), a single value is returned if low and high are both scalars. Otherwise, np.broadcast(low, high).size samples are drawn.
Returns : out ndarray or scalar
Drawn samples from the parameterized uniform distribution.
Discrete uniform distribution, yielding integers.
Discrete uniform distribution over the closed interval [low, high] .
Floats uniformly distributed over [0, 1) .
Convenience function that accepts dimensions as input, e.g., rand(2,2) would generate a 2-by-2 array of floats, uniformly distributed over [0, 1) .
which should be used for new code.
The probability density function of the uniform distribution is
anywhere within the interval [a, b) , and zero elsewhere.
>>> x = np.float32(5*0.99999999) >>> x 5.0
Draw samples from the distribution:
All values are within the given interval:
>>> np.all(s >= -1) True >>> np.all(s 0) True
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 15, density=True) >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') >>> plt.show()
numpy.random.uniform#
Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform .
New code should use the uniform method of a Generator instance instead; please see the Quick Start .
Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.
high float or array_like of floats
Upper boundary of the output interval. All values generated will be less than or equal to high. The high limit may be included in the returned array of floats due to floating-point rounding in the equation low + (high-low) * random_sample() . The default value is 1.0.
size int or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k) , then m * n * k samples are drawn. If size is None (default), a single value is returned if low and high are both scalars. Otherwise, np.broadcast(low, high).size samples are drawn.
Returns : out ndarray or scalar
Drawn samples from the parameterized uniform distribution.
Discrete uniform distribution, yielding integers.
Discrete uniform distribution over the closed interval [low, high] .
Floats uniformly distributed over [0, 1) .
Convenience function that accepts dimensions as input, e.g., rand(2,2) would generate a 2-by-2 array of floats, uniformly distributed over [0, 1) .
which should be used for new code.
The probability density function of the uniform distribution is
anywhere within the interval [a, b) , and zero elsewhere.
>>> x = np.float32(5*0.99999999) >>> x 5.0
Draw samples from the distribution:
All values are within the given interval:
>>> np.all(s >= -1) True >>> np.all(s 0) True
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 15, density=True) >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') >>> plt.show()
numpy.random.uniform#
Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform .
New code should use the uniform method of a Generator instance instead; please see the Quick Start .
Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.
high float or array_like of floats
Upper boundary of the output interval. All values generated will be less than or equal to high. The high limit may be included in the returned array of floats due to floating-point rounding in the equation low + (high-low) * random_sample() . The default value is 1.0.
size int or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k) , then m * n * k samples are drawn. If size is None (default), a single value is returned if low and high are both scalars. Otherwise, np.broadcast(low, high).size samples are drawn.
Returns : out ndarray or scalar
Drawn samples from the parameterized uniform distribution.
Discrete uniform distribution, yielding integers.
Discrete uniform distribution over the closed interval [low, high] .
Floats uniformly distributed over [0, 1) .
Convenience function that accepts dimensions as input, e.g., rand(2,2) would generate a 2-by-2 array of floats, uniformly distributed over [0, 1) .
which should be used for new code.
The probability density function of the uniform distribution is
anywhere within the interval [a, b) , and zero elsewhere.
>>> x = np.float32(5*0.99999999) >>> x 5.0
Draw samples from the distribution:
All values are within the given interval:
>>> np.all(s >= -1) True >>> np.all(s 0) True
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 15, density=True) >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') >>> plt.show()
numpy.random.uniform#
Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform .
New code should use the uniform method of a Generator instance instead; please see the Quick Start .
Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.
high float or array_like of floats
Upper boundary of the output interval. All values generated will be less than or equal to high. The high limit may be included in the returned array of floats due to floating-point rounding in the equation low + (high-low) * random_sample() . The default value is 1.0.
size int or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k) , then m * n * k samples are drawn. If size is None (default), a single value is returned if low and high are both scalars. Otherwise, np.broadcast(low, high).size samples are drawn.
Returns : out ndarray or scalar
Drawn samples from the parameterized uniform distribution.
Discrete uniform distribution, yielding integers.
Discrete uniform distribution over the closed interval [low, high] .
Floats uniformly distributed over [0, 1) .
Convenience function that accepts dimensions as input, e.g., rand(2,2) would generate a 2-by-2 array of floats, uniformly distributed over [0, 1) .
which should be used for new code.
The probability density function of the uniform distribution is
anywhere within the interval [a, b) , and zero elsewhere.
>>> x = np.float32(5*0.99999999) >>> x 5.0
Draw samples from the distribution:
All values are within the given interval:
>>> np.all(s >= -1) True >>> np.all(s 0) True
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 15, density=True) >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') >>> plt.show()