diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/oldnumeric/random_array.py | 34 | ||||
| -rw-r--r-- | numpy/random/mtrand/mtrand.pyx | 156 |
2 files changed, 95 insertions, 95 deletions
diff --git a/numpy/oldnumeric/random_array.py b/numpy/oldnumeric/random_array.py index 73564811d..e84aedf1e 100644 --- a/numpy/oldnumeric/random_array.py +++ b/numpy/oldnumeric/random_array.py @@ -10,7 +10,7 @@ __all__ = ['ArgumentError','F','beta','binomial','chi_square', 'exponential', ArgumentError = ValueError import numpy.random.mtrand as mt -import numpy as Numeric +import numpy as np def seed(x=0, y=0): if (x == 0 or y == 0): @@ -48,8 +48,8 @@ def randint(minimum, maximum=None, shape=[]): if not isinstance(maximum, int): raise ArgumentError, "randint requires second argument integer" a = ((maximum-minimum)* random(shape)) - if isinstance(a, Numeric.ndarray): - return minimum + a.astype(Numeric.int) + if isinstance(a, np.ndarray): + return minimum + a.astype(np.int) else: return minimum + int(a) @@ -164,7 +164,7 @@ def multinomial(trials, probs, shape=[]): trials is the number of trials in each multinomial distribution. probs is a one dimensional array. There are len(prob)+1 events. prob[i] is the probability of the i-th event, 0<=i<len(prob). - The probability of event len(prob) is 1.-Numeric.sum(prob). + The probability of event len(prob) is 1.-np.sum(prob). The first form returns a single 1-D array containing one multinomially distributed vector. @@ -186,14 +186,14 @@ def poisson(mean, shape=[]): def mean_var_test(x, type, mean, var, skew=[]): n = len(x) * 1.0 - x_mean = Numeric.sum(x,axis=0)/n + x_mean = np.sum(x,axis=0)/n x_minus_mean = x - x_mean - x_var = Numeric.sum(x_minus_mean*x_minus_mean,axis=0)/(n-1.0) + x_var = np.sum(x_minus_mean*x_minus_mean,axis=0)/(n-1.0) print "\nAverage of ", len(x), type print "(should be about ", mean, "):", x_mean print "Variance of those random numbers (should be about ", var, "):", x_var if skew != []: - x_skew = (Numeric.sum(x_minus_mean*x_minus_mean*x_minus_mean,axis=0)/9998.)/x_var**(3./2.) + x_skew = (np.sum(x_minus_mean*x_minus_mean*x_minus_mean,axis=0)/9998.)/x_var**(3./2.) print "Skewness of those random numbers (should be about ", skew, "):", x_skew def test(): @@ -203,17 +203,17 @@ def test(): if (obj2[1] - obj[1]).any(): raise SystemExit, "Failed seed test." print "First random number is", random() - print "Average of 10000 random numbers is", Numeric.sum(random(10000),axis=0)/10000. + print "Average of 10000 random numbers is", np.sum(random(10000),axis=0)/10000. x = random([10,1000]) if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000: raise SystemExit, "random returned wrong shape" x.shape = (10000,) - print "Average of 100 by 100 random numbers is", Numeric.sum(x,axis=0)/10000. + print "Average of 100 by 100 random numbers is", np.sum(x,axis=0)/10000. y = uniform(0.5,0.6, (1000,10)) if len(y.shape) !=2 or y.shape[0] != 1000 or y.shape[1] != 10: raise SystemExit, "uniform returned wrong shape" y.shape = (10000,) - if Numeric.minimum.reduce(y) <= 0.5 or Numeric.maximum.reduce(y) >= 0.6: + if np.minimum.reduce(y) <= 0.5 or np.maximum.reduce(y) >= 0.6: raise SystemExit, "uniform returned out of desired range" print "randint(1, 10, shape=[50])" print randint(1, 10, shape=[50]) @@ -229,26 +229,26 @@ def test(): mean_var_test(x, "normally distributed numbers with mean 2 and variance %f"%(s**2,), 2, s**2, 0) x = exponential(3, 10000) mean_var_test(x, "random numbers exponentially distributed with mean %f"%(s,), s, s**2, 2) - x = multivariate_normal(Numeric.array([10,20]), Numeric.array(([1,2],[2,4]))) + x = multivariate_normal(np.array([10,20]), np.array(([1,2],[2,4]))) print "\nA multivariate normal", x if x.shape != (2,): raise SystemExit, "multivariate_normal returned wrong shape" - x = multivariate_normal(Numeric.array([10,20]), Numeric.array([[1,2],[2,4]]), [4,3]) + x = multivariate_normal(np.array([10,20]), np.array([[1,2],[2,4]]), [4,3]) print "A 4x3x2 array containing multivariate normals" print x if x.shape != (4,3,2): raise SystemExit, "multivariate_normal returned wrong shape" - x = multivariate_normal(Numeric.array([-100,0,100]), Numeric.array([[3,2,1],[2,2,1],[1,1,1]]), 10000) - x_mean = Numeric.sum(x,axis=0)/10000. + x = multivariate_normal(np.array([-100,0,100]), np.array([[3,2,1],[2,2,1],[1,1,1]]), 10000) + x_mean = np.sum(x,axis=0)/10000. print "Average of 10000 multivariate normals with mean [-100,0,100]" print x_mean x_minus_mean = x - x_mean print "Estimated covariance of 10000 multivariate normals with covariance [[3,2,1],[2,2,1],[1,1,1]]" - print Numeric.dot(Numeric.transpose(x_minus_mean),x_minus_mean)/9999. + print np.dot(np.transpose(x_minus_mean),x_minus_mean)/9999. x = beta(5.0, 10.0, 10000) mean_var_test(x, "beta(5.,10.) random numbers", 0.333, 0.014) x = gamma(.01, 2., 10000) mean_var_test(x, "gamma(.01,2.) random numbers", 2*100, 2*100*100) x = chi_square(11., 10000) - mean_var_test(x, "chi squared random numbers with 11 degrees of freedom", 11, 22, 2*Numeric.sqrt(2./11.)) + mean_var_test(x, "chi squared random numbers with 11 degrees of freedom", 11, 22, 2*np.sqrt(2./11.)) x = F(5., 10., 10000) mean_var_test(x, "F random numbers with 5 and 10 degrees of freedom", 1.25, 1.35) x = poisson(50., 10000) @@ -260,7 +260,7 @@ def test(): print "\nEach row is the result of 16 multinomial trials with probabilities [0.1, 0.5, 0.1 0.3]:" x = multinomial(16, [0.1, 0.5, 0.1], 8) print x - print "Mean = ", Numeric.sum(x,axis=0)/8. + print "Mean = ", np.sum(x,axis=0)/8. if __name__ == '__main__': test() diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx index f54a576d9..1551270a4 100644 --- a/numpy/random/mtrand/mtrand.pyx +++ b/numpy/random/mtrand/mtrand.pyx @@ -119,7 +119,7 @@ cdef extern from "initarray.h": # Initialize numpy import_array() -import numpy as _sp +import numpy as np cdef object cont0_array(rk_state *state, rk_cont0 func, object size): cdef double *array_data @@ -130,7 +130,7 @@ cdef object cont0_array(rk_state *state, rk_cont0 func, object size): if size is None: return func(state) else: - array = <ndarray>_sp.empty(size, _sp.float64) + array = <ndarray>np.empty(size, np.float64) length = PyArray_SIZE(array) array_data = <double *>array.data for i from 0 <= i < length: @@ -147,7 +147,7 @@ cdef object cont1_array_sc(rk_state *state, rk_cont1 func, object size, double a if size is None: return func(state, a) else: - array = <ndarray>_sp.empty(size, _sp.float64) + array = <ndarray>np.empty(size, np.float64) length = PyArray_SIZE(array) array_data = <double *>array.data for i from 0 <= i < length: @@ -172,7 +172,7 @@ cdef object cont1_array(rk_state *state, rk_cont1 func, object size, ndarray oa) array_data[i] = func(state, (<double *>(itera.dataptr))[0]) PyArray_ITER_NEXT(itera) else: - array = <ndarray>_sp.empty(size, _sp.float64) + array = <ndarray>np.empty(size, np.float64) array_data = <double *>array.data multi = <broadcast>PyArray_MultiIterNew(2, <void *>array, <void *>oa) @@ -194,7 +194,7 @@ cdef object cont2_array_sc(rk_state *state, rk_cont2 func, object size, double a if size is None: return func(state, a, b) else: - array = <ndarray>_sp.empty(size, _sp.float64) + array = <ndarray>np.empty(size, np.float64) length = PyArray_SIZE(array) array_data = <double *>array.data for i from 0 <= i < length: @@ -222,7 +222,7 @@ cdef object cont2_array(rk_state *state, rk_cont2 func, object size, array_data[i] = func(state, oa_data[0], ob_data[0]) PyArray_MultiIter_NEXT(multi) else: - array = <ndarray>_sp.empty(size, _sp.float64) + array = <ndarray>np.empty(size, np.float64) array_data = <double *>array.data multi = <broadcast>PyArray_MultiIterNew(3, <void*>array, <void *>oa, <void *>ob) if (multi.size != PyArray_SIZE(array)): @@ -246,7 +246,7 @@ cdef object cont3_array_sc(rk_state *state, rk_cont3 func, object size, double a if size is None: return func(state, a, b, c) else: - array = <ndarray>_sp.empty(size, _sp.float64) + array = <ndarray>np.empty(size, np.float64) length = PyArray_SIZE(array) array_data = <double *>array.data for i from 0 <= i < length: @@ -276,7 +276,7 @@ cdef object cont3_array(rk_state *state, rk_cont3 func, object size, ndarray oa, array_data[i] = func(state, oa_data[0], ob_data[0], oc_data[0]) PyArray_MultiIter_NEXT(multi) else: - array = <ndarray>_sp.empty(size, _sp.float64) + array = <ndarray>np.empty(size, np.float64) array_data = <double *>array.data multi = <broadcast>PyArray_MultiIterNew(4, <void*>array, <void *>oa, <void *>ob, <void *>oc) @@ -299,7 +299,7 @@ cdef object disc0_array(rk_state *state, rk_disc0 func, object size): if size is None: return func(state) else: - array = <ndarray>_sp.empty(size, int) + array = <ndarray>np.empty(size, int) length = PyArray_SIZE(array) array_data = <long *>array.data for i from 0 <= i < length: @@ -315,7 +315,7 @@ cdef object discnp_array_sc(rk_state *state, rk_discnp func, object size, long n if size is None: return func(state, n, p) else: - array = <ndarray>_sp.empty(size, int) + array = <ndarray>np.empty(size, int) length = PyArray_SIZE(array) array_data = <long *>array.data for i from 0 <= i < length: @@ -341,7 +341,7 @@ cdef object discnp_array(rk_state *state, rk_discnp func, object size, ndarray o array_data[i] = func(state, on_data[0], op_data[0]) PyArray_MultiIter_NEXT(multi) else: - array = <ndarray>_sp.empty(size, int) + array = <ndarray>np.empty(size, int) array_data = <long *>array.data multi = <broadcast>PyArray_MultiIterNew(3, <void*>array, <void *>on, <void *>op) if (multi.size != PyArray_SIZE(array)): @@ -365,7 +365,7 @@ cdef object discnmN_array_sc(rk_state *state, rk_discnmN func, object size, if size is None: return func(state, n, m, N) else: - array = <ndarray>_sp.empty(size, int) + array = <ndarray>np.empty(size, int) length = PyArray_SIZE(array) array_data = <long *>array.data for i from 0 <= i < length: @@ -394,7 +394,7 @@ cdef object discnmN_array(rk_state *state, rk_discnmN func, object size, array_data[i] = func(state, on_data[0], om_data[0], oN_data[0]) PyArray_MultiIter_NEXT(multi) else: - array = <ndarray>_sp.empty(size, int) + array = <ndarray>np.empty(size, int) array_data = <long *>array.data multi = <broadcast>PyArray_MultiIterNew(4, <void*>array, <void *>on, <void *>om, <void *>oN) @@ -418,7 +418,7 @@ cdef object discd_array_sc(rk_state *state, rk_discd func, object size, double a if size is None: return func(state, a) else: - array = <ndarray>_sp.empty(size, int) + array = <ndarray>np.empty(size, int) length = PyArray_SIZE(array) array_data = <long *>array.data for i from 0 <= i < length: @@ -443,7 +443,7 @@ cdef object discd_array(rk_state *state, rk_discd func, object size, ndarray oa) array_data[i] = func(state, (<double *>(itera.dataptr))[0]) PyArray_ITER_NEXT(itera) else: - array = <ndarray>_sp.empty(size, int) + array = <ndarray>np.empty(size, int) array_data = <long *>array.data multi = <broadcast>PyArray_MultiIterNew(2, <void *>array, <void *>oa) if (multi.size != PyArray_SIZE(array)): @@ -512,7 +512,7 @@ cdef class RandomState: errcode = rk_randomseed(self.internal_state) elif type(seed) is int: rk_seed(seed, self.internal_state) - elif isinstance(seed, _sp.integer): + elif isinstance(seed, np.integer): iseed = int(seed) rk_seed(iseed, self.internal_state) else: @@ -526,9 +526,9 @@ cdef class RandomState: get_state() -> ('MT19937', int key[624], int pos, int has_gauss, float cached_gaussian) """ cdef ndarray state "arrayObject_state" - state = <ndarray>_sp.empty(624, _sp.uint) + state = <ndarray>np.empty(624, np.uint) memcpy(<void*>(state.data), <void*>(self.internal_state.key), 624*sizeof(long)) - state = <ndarray>_sp.asarray(state, _sp.uint32) + state = <ndarray>np.asarray(state, np.uint32) return ('MT19937', state, self.internal_state.pos, self.internal_state.has_gauss, self.internal_state.gauss) @@ -575,7 +575,7 @@ cdef class RandomState: self.set_state(state) def __reduce__(self): - return (_sp.random.__RandomState_ctor, (), self.get_state()) + return (np.random.__RandomState_ctor, (), self.get_state()) # Basic distributions: def random_sample(self, size=None): @@ -619,7 +619,7 @@ cdef class RandomState: if size is None: return <long>rk_interval(diff, self.internal_state) + lo else: - array = <ndarray>_sp.empty(size, int) + array = <ndarray>np.empty(size, int) length = PyArray_SIZE(array) array_data = <long *>array.data for i from 0 <= i < length: @@ -653,7 +653,7 @@ cdef class RandomState: PyErr_Clear() olow = <ndarray>PyArray_FROM_OTF(low, NPY_DOUBLE, NPY_ALIGNED) ohigh = <ndarray>PyArray_FROM_OTF(high, NPY_DOUBLE, NPY_ALIGNED) - temp = _sp.subtract(ohigh, olow) + temp = np.subtract(ohigh, olow) Py_INCREF(temp) # needed to get around Pyrex's automatic reference-counting # rules because EnsureArray steals a reference odiff = <ndarray>PyArray_EnsureArray(temp) @@ -729,7 +729,7 @@ cdef class RandomState: oloc = <ndarray>PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED) oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oscale, 0)): + if np.any(np.less_equal(oscale, 0)): raise ValueError("scale <= 0") return cont2_array(self.internal_state, rk_normal, size, oloc, oscale) @@ -754,9 +754,9 @@ cdef class RandomState: oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED) ob = <ndarray>PyArray_FROM_OTF(b, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oa, 0)): + if np.any(np.less_equal(oa, 0)): raise ValueError("a <= 0") - if _sp.any(_sp.less_equal(ob, 0)): + if np.any(np.less_equal(ob, 0)): raise ValueError("b <= 0") return cont2_array(self.internal_state, rk_beta, size, oa, ob) @@ -777,7 +777,7 @@ cdef class RandomState: PyErr_Clear() oscale = <ndarray> PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oscale, 0.0)): + if np.any(np.less_equal(oscale, 0.0)): raise ValueError("scale <= 0") return cont1_array(self.internal_state, rk_exponential, size, oscale) @@ -804,7 +804,7 @@ cdef class RandomState: PyErr_Clear() oshape = <ndarray> PyArray_FROM_OTF(shape, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oshape, 0.0)): + if np.any(np.less_equal(oshape, 0.0)): raise ValueError("shape <= 0") return cont1_array(self.internal_state, rk_standard_gamma, size, oshape) @@ -828,9 +828,9 @@ cdef class RandomState: PyErr_Clear() oshape = <ndarray>PyArray_FROM_OTF(shape, NPY_DOUBLE, NPY_ALIGNED) oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oshape, 0.0)): + if np.any(np.less_equal(oshape, 0.0)): raise ValueError("shape <= 0") - if _sp.any(_sp.less_equal(oscale, 0.0)): + if np.any(np.less_equal(oscale, 0.0)): raise ValueError("scale <= 0") return cont2_array(self.internal_state, rk_gamma, size, oshape, oscale) @@ -855,9 +855,9 @@ cdef class RandomState: odfnum = <ndarray>PyArray_FROM_OTF(dfnum, NPY_DOUBLE, NPY_ALIGNED) odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(odfnum, 0.0)): + if np.any(np.less_equal(odfnum, 0.0)): raise ValueError("dfnum <= 0") - if _sp.any(_sp.less_equal(odfden, 0.0)): + if np.any(np.less_equal(odfden, 0.0)): raise ValueError("dfden <= 0") return cont2_array(self.internal_state, rk_f, size, odfnum, odfden) @@ -888,11 +888,11 @@ cdef class RandomState: odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ALIGNED) ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(odfnum, 1.0)): + if np.any(np.less_equal(odfnum, 1.0)): raise ValueError("dfnum <= 1") - if _sp.any(_sp.less_equal(odfden, 0.0)): + if np.any(np.less_equal(odfden, 0.0)): raise ValueError("dfden <= 0") - if _sp.any(_sp.less(ononc, 0.0)): + if np.any(np.less(ononc, 0.0)): raise ValueError("nonc < 0") return cont3_array(self.internal_state, rk_noncentral_f, size, odfnum, odfden, ononc) @@ -914,7 +914,7 @@ cdef class RandomState: PyErr_Clear() odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(odf, 0.0)): + if np.any(np.less_equal(odf, 0.0)): raise ValueError("df <= 0") return cont1_array(self.internal_state, rk_chisquare, size, odf) @@ -939,9 +939,9 @@ cdef class RandomState: odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED) ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(odf, 0.0)): + if np.any(np.less_equal(odf, 0.0)): raise ValueError("df <= 1") - if _sp.any(_sp.less_equal(ononc, 0.0)): + if np.any(np.less_equal(ononc, 0.0)): raise ValueError("nonc < 0") return cont2_array(self.internal_state, rk_noncentral_chisquare, size, odf, ononc) @@ -970,7 +970,7 @@ cdef class RandomState: PyErr_Clear() odf = <ndarray> PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(odf, 0.0)): + if np.any(np.less_equal(odf, 0.0)): raise ValueError("df <= 0") return cont1_array(self.internal_state, rk_standard_t, size, odf) @@ -994,7 +994,7 @@ cdef class RandomState: omu = <ndarray> PyArray_FROM_OTF(mu, NPY_DOUBLE, NPY_ALIGNED) okappa = <ndarray> PyArray_FROM_OTF(kappa, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less(okappa, 0.0)): + if np.any(np.less(okappa, 0.0)): raise ValueError("kappa < 0") return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa) @@ -1015,7 +1015,7 @@ cdef class RandomState: PyErr_Clear() oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oa, 0.0)): + if np.any(np.less_equal(oa, 0.0)): raise ValueError("a <= 0") return cont1_array(self.internal_state, rk_pareto, size, oa) @@ -1036,7 +1036,7 @@ cdef class RandomState: PyErr_Clear() oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oa, 0.0)): + if np.any(np.less_equal(oa, 0.0)): raise ValueError("a <= 0") return cont1_array(self.internal_state, rk_weibull, size, oa) @@ -1057,7 +1057,7 @@ cdef class RandomState: PyErr_Clear() oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oa, 0.0)): + if np.any(np.less_equal(oa, 0.0)): raise ValueError("a <= 0") return cont1_array(self.internal_state, rk_power, size, oa) @@ -1079,7 +1079,7 @@ cdef class RandomState: PyErr_Clear() oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED) oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oscale, 0.0)): + if np.any(np.less_equal(oscale, 0.0)): raise ValueError("scale <= 0") return cont2_array(self.internal_state, rk_laplace, size, oloc, oscale) @@ -1101,7 +1101,7 @@ cdef class RandomState: PyErr_Clear() oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED) oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oscale, 0.0)): + if np.any(np.less_equal(oscale, 0.0)): raise ValueError("scale <= 0") return cont2_array(self.internal_state, rk_gumbel, size, oloc, oscale) @@ -1123,7 +1123,7 @@ cdef class RandomState: PyErr_Clear() oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED) oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oscale, 0.0)): + if np.any(np.less_equal(oscale, 0.0)): raise ValueError("scale <= 0") return cont2_array(self.internal_state, rk_logistic, size, oloc, oscale) @@ -1152,7 +1152,7 @@ cdef class RandomState: omean = PyArray_FROM_OTF(mean, NPY_DOUBLE, NPY_ALIGNED) osigma = PyArray_FROM_OTF(sigma, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(osigma, 0.0)): + if np.any(np.less_equal(osigma, 0.0)): raise ValueError("sigma <= 0.0") return cont2_array(self.internal_state, rk_lognormal, size, omean, osigma) @@ -1174,7 +1174,7 @@ cdef class RandomState: PyErr_Clear() oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oscale, 0.0)): + if np.any(np.less_equal(oscale, 0.0)): raise ValueError("scale <= 0.0") return cont1_array(self.internal_state, rk_rayleigh, size, oscale) @@ -1198,9 +1198,9 @@ cdef class RandomState: PyErr_Clear() omean = PyArray_FROM_OTF(mean, NPY_DOUBLE, NPY_ALIGNED) oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(omean,0.0)): + if np.any(np.less_equal(omean,0.0)): raise ValueError("mean <= 0.0") - elif _sp.any(_sp.less_equal(oscale,0.0)): + elif np.any(np.less_equal(oscale,0.0)): raise ValueError("scale <= 0.0") return cont2_array(self.internal_state, rk_wald, size, omean, oscale) @@ -1233,11 +1233,11 @@ cdef class RandomState: omode = <ndarray>PyArray_FROM_OTF(mode, NPY_DOUBLE, NPY_ALIGNED) oright = <ndarray>PyArray_FROM_OTF(right, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.greater(oleft, omode)): + if np.any(np.greater(oleft, omode)): raise ValueError("left > mode") - if _sp.any(_sp.greater(omode, oright)): + if np.any(np.greater(omode, oright)): raise ValueError("mode > right") - if _sp.any(_sp.equal(oleft, oright)): + if np.any(np.equal(oleft, oright)): raise ValueError("left == right") return cont3_array(self.internal_state, rk_triangular, size, oleft, omode, oright) @@ -1267,11 +1267,11 @@ cdef class RandomState: on = <ndarray>PyArray_FROM_OTF(n, NPY_LONG, NPY_ALIGNED) op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(n, 0)): + if np.any(np.less_equal(n, 0)): raise ValueError("n <= 0") - if _sp.any(_sp.less(p, 0)): + if np.any(np.less(p, 0)): raise ValueError("p < 0") - if _sp.any(_sp.greater(p, 1)): + if np.any(np.greater(p, 1)): raise ValueError("p > 1") return discnp_array(self.internal_state, rk_binomial, size, on, op) @@ -1301,11 +1301,11 @@ cdef class RandomState: on = <ndarray>PyArray_FROM_OTF(n, NPY_LONG, NPY_ALIGNED) op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(n, 0)): + if np.any(np.less_equal(n, 0)): raise ValueError("n <= 0") - if _sp.any(_sp.less(p, 0)): + if np.any(np.less(p, 0)): raise ValueError("p < 0") - if _sp.any(_sp.greater(p, 1)): + if np.any(np.greater(p, 1)): raise ValueError("p > 1") return discnp_array(self.internal_state, rk_negative_binomial, size, on, op) @@ -1326,7 +1326,7 @@ cdef class RandomState: PyErr_Clear() olam = <ndarray>PyArray_FROM_OTF(lam, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less(olam, 0)): + if np.any(np.less(olam, 0)): raise ValueError("lam < 0") return discd_array(self.internal_state, rk_poisson, size, olam) @@ -1347,7 +1347,7 @@ cdef class RandomState: PyErr_Clear() oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less_equal(oa, 1.0)): + if np.any(np.less_equal(oa, 1.0)): raise ValueError("a <= 1.0") return discd_array(self.internal_state, rk_zipf, size, oa) @@ -1372,9 +1372,9 @@ cdef class RandomState: op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less(op, 0.0)): + if np.any(np.less(op, 0.0)): raise ValueError("p < 0.0") - if _sp.any(_sp.greater(op, 1.0)): + if np.any(np.greater(op, 1.0)): raise ValueError("p > 1.0") return discd_array(self.internal_state, rk_geometric, size, op) @@ -1412,13 +1412,13 @@ cdef class RandomState: ongood = <ndarray>PyArray_FROM_OTF(ngood, NPY_LONG, NPY_ALIGNED) onbad = <ndarray>PyArray_FROM_OTF(nbad, NPY_LONG, NPY_ALIGNED) onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ALIGNED) - if _sp.any(_sp.less(ongood, 1)): + if np.any(np.less(ongood, 1)): raise ValueError("ngood < 1") - if _sp.any(_sp.less(onbad, 1)): + if np.any(np.less(onbad, 1)): raise ValueError("nbad < 1") - if _sp.any(_sp.less(onsample, 1)): + if np.any(np.less(onsample, 1)): raise ValueError("nsample < 1") - if _sp.any(_sp.less(_sp.add(ongood, onbad),onsample)): + if np.any(np.less(np.add(ongood, onbad),onsample)): raise ValueError("ngood + nbad < nsample") return discnmN_array(self.internal_state, rk_hypergeometric, size, ongood, onbad, onsample) @@ -1442,9 +1442,9 @@ cdef class RandomState: PyErr_Clear() op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED) - if _sp.any(_sp.less(op, 0.0)): + if np.any(np.less(op, 0.0)): raise ValueError("p < 0.0") - if _sp.any(_sp.greater(op, 1.0)): + if np.any(np.greater(op, 1.0)): raise ValueError("p > 1.0") return discd_array(self.internal_state, rk_logseries, size, op) @@ -1467,8 +1467,8 @@ cdef class RandomState: normal. """ # Check preconditions on arguments - mean = _sp.array(mean) - cov = _sp.array(cov) + mean = np.array(mean) + cov = np.array(cov) if size is None: shape = [] else: @@ -1487,8 +1487,8 @@ cdef class RandomState: # Create a matrix of independent standard normally distributed random # numbers. The matrix has rows with the same length as mean and as # many rows are necessary to form a matrix of shape final_shape. - x = self.standard_normal(_sp.multiply.reduce(final_shape)) - x.shape = (_sp.multiply.reduce(final_shape[0:len(final_shape)-1]), + x = self.standard_normal(np.multiply.reduce(final_shape)) + x.shape = (np.multiply.reduce(final_shape[0:len(final_shape)-1]), mean.shape[0]) # Transform matrix of standard normals into matrix where each row # contains multivariate normals with the desired covariance. @@ -1500,10 +1500,10 @@ cdef class RandomState: from numpy.dual import svd # XXX: we really should be doing this by Cholesky decomposition (u,s,v) = svd(cov) - x = _sp.dot(x*_sp.sqrt(s),v) + x = np.dot(x*np.sqrt(s),v) # The rows of x now have the correct covariance but mean 0. Add # mean to each row. Then each row will have mean mean. - _sp.add(mean,x,x) + np.add(mean,x,x) x.shape = tuple(final_shape) return x @@ -1537,7 +1537,7 @@ cdef class RandomState: else: shape = size + (d,) - multin = _sp.zeros(shape, int) + multin = np.zeros(shape, int) mnarr = <ndarray>multin mnix = <long*>mnarr.data i = 0 @@ -1629,7 +1629,7 @@ cdef class RandomState: else: shape = size + (k,) - diric = _sp.zeros(shape, _sp.float64) + diric = np.zeros(shape, np.float64) val_arr = <ndarray>diric val_data= <double*>val_arr.data @@ -1688,10 +1688,10 @@ cdef class RandomState: permutation(x) """ - if isinstance(x, (int, _sp.integer)): - arr = _sp.arange(x) + if isinstance(x, (int, np.integer)): + arr = np.arange(x) else: - arr = _sp.array(x) + arr = np.array(x) self.shuffle(arr) return arr |