Python random.normal方法代碼示例

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def random_rot(dim):
    """Return a random rotation matrix, drawn from the Haar distribution
    (the only uniform distribution on SO(n)).
    The algorithm is described in the paper
    Stewart, G.W., 'The efficient generation of random orthogonal
    matrices with an application to condition estimators', SIAM Journal
    on Numerical Analysis, 17(3), pp. 403-409, 1980.
    For more information see
    http://en.wikipedia.org/wiki/Orthogonal_matrix#Randomization"""
    H = eye(dim)
    D = ones((dim,))
    for n in range(1, dim):
        x = normal(size=(dim-n+1,))
        D[n-1] = sign(x[0])
        x[0] -= D[n-1]*sqrt((x*x).sum())
        # Householder transformation

        Hx = eye(dim-n+1) - 2.*outer(x, x)/(x*x).sum()
        mat = eye(dim)
        mat[n-1:,n-1:] = Hx
        H = dot(H, mat)
    # Fix the last sign such that the determinant is 1
    D[-1] = -D.prod()
    H = (D*H.T).T
    return H 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def gen_wave():
    """ Generate a synthetic wave by adding up a few sine waves and some noise
    :return: the final wave
    """
    t = np.arange(0.0, 10.0, 0.01)
    wave1 = sin(2 * 2 * pi * t)
    noise = random.normal(0, 0.1, len(t))
    wave1 = wave1 + noise
    print("wave1", len(wave1))
    wave2 = sin(2 * pi * t)
    print("wave2", len(wave2))
    t_rider = arange(0.0, 0.5, 0.01)
    wave3 = sin(10 * pi * t_rider)
    print("wave3", len(wave3))
    insert = round(0.8 * len(t))
    wave1[insert:insert + 50] = wave1[insert:insert + 50] + wave3
    return wave1 + wave2 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def test_gaussian2d_fit():
    mx0 = 0.1
    my0 = 0.9
    sigx0 = 0.4
    sigy0 = 0.25

    Size = 500
    sx = R.normal(size=Size, loc=mx0, scale=sigx0)
    sy = R.normal(size=Size, loc=my0, scale=sigy0)

    mux, sigmax, muy, sigmay = gaussian2d_fit(sx, sy)

    plot(sx, sy, 'o', alpha=0.2, mew=0)

    X,Y = np.mgrid[sx.min()-1:sx.max()+1:200j, sy.min()-1:sy.max()+1:200j]

    def gauss2d(X,Y, mx, my, sigx, sigy):
        return np.exp(-((X-mx)**2)/(2*sigx**2))*np.exp(-((Y-my)**2)/(2*sigy**2))

    contour(X,Y,gauss2d(X,Y,mux,muy,sigmax,sigmay))

    plot(mx0,my0, 'ok', mew=0, ms=10)
    plot(mux,muy, 'x', mew=2, ms=10, color='green') 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def test_gaussian_fit():
    m0 = 0.1
    s0 = 0.4
    size = 500

    s = R.normal(size=size, loc=m0, scale=s0)
    #s = s[s<0.4]
    mu, sig = gaussian_fit(s)
    mu1, sig1 = S.norm.fit(s)
    mu2, sig2 = gaussian_fit_ml(s)

    print("ECDF ", mu, sig)
    print("ML         ", mu1, sig1)
    print("ML (manual)", mu2, sig2)

    H = np.histogram(s, bins=20, density=True)
    h = H[0]
    bw = H[1][1] - H[1][0]
    #bins_c = H[1][:-1]+0.5*bw
    bar(H[1][:-1], H[0], bw, alpha=0.3)

    x = np.r_[s.min()-1:s.max()+1:200j]
    plot(x, normpdf(x,m0,s0), lw=2, color='grey')
    plot(x, normpdf(x,mu,sig), lw=2, color='r', alpha=0.5)
    plot(x, normpdf(x,mu1,sig1), lw=2, color='b', alpha=0.5) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def step(self):

    #self.r *= BRANCH_DIMINISH
    self.r = self.r - self.tree.branch_diminish

    angle = normal()*self.tree.branch_angle_max

    #da = (1.-1./((self.g+1)**SEARCH_ANGLE_EXP))*angle
    #da = ((1./(ONE + INIT_BRANCH - self.r))**SEARCH_ANGLE_EXP)*angle
    #da = (1.-1./(ONE + INIT_BRANCH - self.r)**SEARCH_ANGLE_EXP)*angle

    scale = self.tree.one+self.tree.root_r-self.r
    da = (1.+scale/self.tree.root_r)**self.tree.branch_angle_exp
    self.a += da*angle

    dx = cos(self.a)*self.tree.stepsize
    dy = sin(self.a)*self.tree.stepsize

    self.x += dx
    self.y += dy
    self.i += 1 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def _test_guassian_comparison():
    '''
    Method to test the _comparE_gaussian function
    '''

    size = 100
    dist1 = normal(loc=0, scale=1, size=size)

    dist2 = normal(loc=0.1, scale=0.9, size=size)
    assert ptest._compare_gaussians(dist1, dist2) == True, "The input distributions are similar."

    dist2 = normal(loc=5, scale=1, size=size)
    assert ptest._compare_gaussians(dist1, dist2) == False, "The input distributions are not similar."

    dist2 = normal(loc=5, scale=5, size=size)
    assert ptest._compare_gaussians(dist1, dist2) == False, "The input distributions are not similar." 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def pw_linear(n_samples=200, n_features=1, n_bkps=3, noise_std=None):
    """
    Return piecewise linear signal and the associated changepoints.

    Args:
        n_samples (int, optional): signal length
        n_features (int, optional): number of covariates
        n_bkps (int, optional): number of change points
        noise_std (float, optional): noise std. If None, no noise is added
    Returns:
        tuple: signal of shape (n_samples, n_features+1), list of breakpoints
    """

    covar = normal(size=(n_samples, n_features))
    linear_coeff, bkps = pw_constant(n_samples=n_samples,
                                     n_bkps=n_bkps,
                                     n_features=n_features,
                                     noise_std=None)
    var = np.sum(linear_coeff * covar, axis=1)
    if noise_std is not None:
        var += normal(scale=noise_std, size=var.shape)
    signal = np.c_[var, covar]
    return signal, bkps 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def _create_regression_table(nrow, id_vars=None, seed=1234, true_label='target', pred_label='p_target'):

    nr.seed(seed)
    mean_value = nr.normal(loc=10, scale=2, size=(nrow, 1))
    error = nr.normal(loc=0, scale=1, size=(nrow, 1))
    true_value = mean_value + error
    regression_matrix = np.hstack((true_value, mean_value))
    regression_matrix = np.abs(regression_matrix)
    colnames = [true_label, pred_label]

    if id_vars is not None:
        if not isinstance(id_vars, list):
            id_vars = [id_vars]
        ncol = len(id_vars)
        id_matrix = _create_id_matrix(nrow, ncol)
        regression_matrix = np.hstack((regression_matrix, id_matrix))
        colnames = colnames + id_vars

    return pd.DataFrame(regression_matrix, columns=colnames) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def set_noise_function(self, proportional=0.0, absolute=0.0):
        '''
        Adds noise to the function.
        
        with the formula::
        
            c'(c,x) = c (1 + s_p p) + s_a a
        
        where s_i are gaussian random variables, p is the proportional noise factor and a is the absolute noise factor, and c is the cost before noise is added
        
        the uncertainty is then::
        
            u = sqrt((cp)^2 + a^2)

        Keyword Args:
            proportional (Optional [float]): the proportional factor. Defaults to 0
            absolute (Optional [float]): the absolute factor. Defaults to 0
        '''
        
        self.noise_prop = proportional
        self.noise_abs = absolute
        self.noise_function = lambda p,c,u : (c *(1 + nr.normal()*self.noise_prop) + nr.normal()*self.noise_abs,np.sqrt((c*self.noise_prop)**2 + (self.noise_abs)**2)) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def make_bd(self):
        "Make a set of 'shaped' random #'s for particle brightness deltas (bd)"
        self.bd = concatenate((
            # These values will dim the particles
            random.normal(
                self.bd_mean - self.bd_mu, self.bd_sigma, 16).astype(int),
            # These values will brighten the particles
            random.normal(
                self.bd_mean + self.bd_mu, self.bd_sigma, 16).astype(int)),
            axis=0) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def make_vel(self):
        "Make a set of velocities to be randomly chosen for emitted particles"
        self.vel = random.normal(self.vel_mu, self.vel_sigma, 16)
        # Make sure nothing's slower than 1/8 pixel / step
        for i, vel in enumerate(self.vel):
            if abs(vel) < 0.125 / self._size:
                if vel < 0:
                    self.vel[i] = -0.125 / self._size
                else:
                    self.vel[i] = 0.125 / self._size 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def setup_method(self, method):

        import matplotlib as mpl
        mpl.rcdefaults()

        self.mpl_ge_2_0_1 = plotting._compat._mpl_ge_2_0_1()
        self.mpl_ge_2_1_0 = plotting._compat._mpl_ge_2_1_0()
        self.mpl_ge_2_2_0 = plotting._compat._mpl_ge_2_2_0()
        self.mpl_ge_2_2_2 = plotting._compat._mpl_ge_2_2_2()
        self.mpl_ge_3_0_0 = plotting._compat._mpl_ge_3_0_0()

        self.bp_n_objects = 7
        self.polycollection_factor = 2
        self.default_figsize = (6.4, 4.8)
        self.default_tick_position = 'left'

        n = 100
        with tm.RNGContext(42):
            gender = np.random.choice(['Male', 'Female'], size=n)
            classroom = np.random.choice(['A', 'B', 'C'], size=n)

            self.hist_df = DataFrame({'gender': gender,
                                      'classroom': classroom,
                                      'height': random.normal(66, 4, size=n),
                                      'weight': random.normal(161, 32, size=n),
                                      'category': random.randint(4, size=n)})

        self.tdf = tm.makeTimeDataFrame()
        self.hexbin_df = DataFrame({"A": np.random.uniform(size=20),
                                    "B": np.random.uniform(size=20),
                                    "C": np.arange(20) + np.random.uniform(
                                        size=20)}) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def tgauss_old(mu=0, sigma=1, trunc=2):
    if sigma == 0:
        return mu
    err = normal(mu, sigma, size=50)
    err_ok = err[abs(err) < trunc*sigma]
    return err_ok[0] 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def create_dataset(num):
    dataset = DataFrame(columns=['x','y'])
    for i in range(num):
        x = float(i)/float(num-1)
        y = np.sin(2*np.pi*x) + normal(scale=0.3)
        dataset = dataset.append(Series([x,y], index=['x','y']),
                                 ignore_index=True)
    return dataset

# 平方根平均二乗誤差（Root mean square error）を計算 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import normal [as 別名]
def create_dataset(num):
    dataset = DataFrame(columns=['x','y'])
    for i in range(num):
        x = float(i)/float(num-1)
        y = np.sin(2.0*np.pi*x) + normal(scale=0.3)
        dataset = dataset.append(Series([x,y], index=['x','y']),
                                 ignore_index=True)
    return dataset

# 事後分布に基づく推定曲線、および、事後分布の平均と分散を計算