HW1 Done

HW1_Stones/env/lib/python3.7/site-packages/numpy/linalg/__init__.py

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+"""
+``numpy.linalg``
+================
+
+The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient
+low level implementations of standard linear algebra algorithms. Those
+libraries may be provided by NumPy itself using C versions of a subset of their
+reference implementations but, when possible, highly optimized libraries that
+take advantage of specialized processor functionality are preferred. Examples
+of such libraries are OpenBLAS, MKL (TM), and ATLAS. Because those libraries
+are multithreaded and processor dependent, environmental variables and external
+packages such as threadpoolctl may be needed to control the number of threads
+or specify the processor architecture.
+
+- OpenBLAS: https://www.openblas.net/
+- threadpoolctl: https://github.com/joblib/threadpoolctl
+
+Please note that the most-used linear algebra functions in NumPy are present in
+the main ``numpy`` namespace rather than in ``numpy.linalg``.  There are:
+``dot``, ``vdot``, ``inner``, ``outer``, ``matmul``, ``tensordot``, ``einsum``,
+``einsum_path`` and ``kron``.
+
+Functions present in numpy.linalg are listed below.
+
+
+Matrix and vector products
+--------------------------
+
+   multi_dot
+   matrix_power
+
+Decompositions
+--------------
+
+   cholesky
+   qr
+   svd
+
+Matrix eigenvalues
+------------------
+
+   eig
+   eigh
+   eigvals
+   eigvalsh
+
+Norms and other numbers
+-----------------------
+
+   norm
+   cond
+   det
+   matrix_rank
+   slogdet
+
+Solving equations and inverting matrices
+----------------------------------------
+
+   solve
+   tensorsolve
+   lstsq
+   inv
+   pinv
+   tensorinv
+
+Exceptions
+----------
+
+   LinAlgError
+
+"""
+from __future__ import division, absolute_import, print_function
+
+# To get sub-modules
+from .linalg import *
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester

HW1_Stones/env/lib/python3.7/site-packages/numpy/linalg/setup.py

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+from __future__ import division, print_function
+
+import os
+import sys
+
+def configuration(parent_package='', top_path=None):
+    from numpy.distutils.misc_util import Configuration
+    from numpy.distutils.system_info import get_info
+    config = Configuration('linalg', parent_package, top_path)
+
+    config.add_data_dir('tests')
+
+    # Configure lapack_lite
+
+    src_dir = 'lapack_lite'
+    lapack_lite_src = [
+        os.path.join(src_dir, 'python_xerbla.c'),
+        os.path.join(src_dir, 'f2c_z_lapack.c'),
+        os.path.join(src_dir, 'f2c_c_lapack.c'),
+        os.path.join(src_dir, 'f2c_d_lapack.c'),
+        os.path.join(src_dir, 'f2c_s_lapack.c'),
+        os.path.join(src_dir, 'f2c_lapack.c'),
+        os.path.join(src_dir, 'f2c_blas.c'),
+        os.path.join(src_dir, 'f2c_config.c'),
+        os.path.join(src_dir, 'f2c.c'),
+    ]
+    all_sources = config.paths(lapack_lite_src)
+
+    if os.environ.get('NPY_USE_BLAS_ILP64', "0") != "0":
+        lapack_info = get_info('lapack_ilp64_opt', 2)
+    else:
+        lapack_info = get_info('lapack_opt', 0)  # and {}
+
+    def get_lapack_lite_sources(ext, build_dir):
+        if not lapack_info:
+            print("### Warning:  Using unoptimized lapack ###")
+            return all_sources
+        else:
+            if sys.platform == 'win32':
+                print("### Warning:  python_xerbla.c is disabled ###")
+                return []
+            return [all_sources[0]]
+
+    config.add_extension(
+        'lapack_lite',
+        sources=['lapack_litemodule.c', get_lapack_lite_sources],
+        depends=['lapack_lite/f2c.h'],
+        extra_info=lapack_info,
+    )
+
+    # umath_linalg module
+    config.add_extension(
+        '_umath_linalg',
+        sources=['umath_linalg.c.src', get_lapack_lite_sources],
+        depends=['lapack_lite/f2c.h'],
+        extra_info=lapack_info,
+        libraries=['npymath'],
+    )
+    return config
+
+if __name__ == '__main__':
+    from numpy.distutils.core import setup
+    setup(configuration=configuration)

HW1_Stones/env/lib/python3.7/site-packages/numpy/linalg/tests/test_build.py

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+from __future__ import division, absolute_import, print_function
+
+from subprocess import PIPE, Popen
+import sys
+import re
+import pytest
+
+from numpy.linalg import lapack_lite
+from numpy.testing import assert_
+
+
+class FindDependenciesLdd(object):
+
+    def __init__(self):
+        self.cmd = ['ldd']
+
+        try:
+            p = Popen(self.cmd, stdout=PIPE, stderr=PIPE)
+            stdout, stderr = p.communicate()
+        except OSError:
+            raise RuntimeError("command %s cannot be run" % self.cmd)
+
+    def get_dependencies(self, lfile):
+        p = Popen(self.cmd + [lfile], stdout=PIPE, stderr=PIPE)
+        stdout, stderr = p.communicate()
+        if not (p.returncode == 0):
+            raise RuntimeError("failed dependencies check for %s" % lfile)
+
+        return stdout
+
+    def grep_dependencies(self, lfile, deps):
+        stdout = self.get_dependencies(lfile)
+
+        rdeps = dict([(dep, re.compile(dep)) for dep in deps])
+        founds = []
+        for l in stdout.splitlines():
+            for k, v in rdeps.items():
+                if v.search(l):
+                    founds.append(k)
+
+        return founds
+
+
+class TestF77Mismatch(object):
+
+    @pytest.mark.skipif(not(sys.platform[:5] == 'linux'),
+                        reason="no fortran compiler on non-Linux platform")
+    def test_lapack(self):
+        f = FindDependenciesLdd()
+        deps = f.grep_dependencies(lapack_lite.__file__,
+                                   [b'libg2c', b'libgfortran'])
+        assert_(len(deps) <= 1,
+                         """Both g77 and gfortran runtimes linked in lapack_lite ! This is likely to
+cause random crashes and wrong results. See numpy INSTALL.txt for more
+information.""")

HW1_Stones/env/lib/python3.7/site-packages/numpy/linalg/tests/test_deprecations.py

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+"""Test deprecation and future warnings.
+
+"""
+from __future__ import division, absolute_import, print_function
+
+import numpy as np
+from numpy.testing import assert_warns
+
+
+def test_qr_mode_full_future_warning():
+    """Check mode='full' FutureWarning.
+
+    In numpy 1.8 the mode options 'full' and 'economic' in linalg.qr were
+    deprecated. The release date will probably be sometime in the summer
+    of 2013.
+
+    """
+    a = np.eye(2)
+    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='full')
+    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='f')
+    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='economic')
+    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='e')

HW1_Stones/env/lib/python3.7/site-packages/numpy/linalg/tests/test_regression.py

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+""" Test functions for linalg module
+"""
+from __future__ import division, absolute_import, print_function
+
+import warnings
+
+import numpy as np
+from numpy import linalg, arange, float64, array, dot, transpose
+from numpy.testing import (
+    assert_, assert_raises, assert_equal, assert_array_equal,
+    assert_array_almost_equal, assert_array_less
+)
+
+
+class TestRegression(object):
+
+    def test_eig_build(self):
+        # Ticket #652
+        rva = array([1.03221168e+02 + 0.j,
+                     -1.91843603e+01 + 0.j,
+                     -6.04004526e-01 + 15.84422474j,
+                     -6.04004526e-01 - 15.84422474j,
+                     -1.13692929e+01 + 0.j,
+                     -6.57612485e-01 + 10.41755503j,
+                     -6.57612485e-01 - 10.41755503j,
+                     1.82126812e+01 + 0.j,
+                     1.06011014e+01 + 0.j,
+                     7.80732773e+00 + 0.j,
+                     -7.65390898e-01 + 0.j,
+                     1.51971555e-15 + 0.j,
+                     -1.51308713e-15 + 0.j])
+        a = arange(13 * 13, dtype=float64)
+        a.shape = (13, 13)
+        a = a % 17
+        va, ve = linalg.eig(a)
+        va.sort()
+        rva.sort()
+        assert_array_almost_equal(va, rva)
+
+    def test_eigh_build(self):
+        # Ticket 662.
+        rvals = [68.60568999, 89.57756725, 106.67185574]
+
+        cov = array([[77.70273908,   3.51489954,  15.64602427],
+                     [3.51489954,  88.97013878,  -1.07431931],
+                     [15.64602427,  -1.07431931,  98.18223512]])
+
+        vals, vecs = linalg.eigh(cov)
+        assert_array_almost_equal(vals, rvals)
+
+    def test_svd_build(self):
+        # Ticket 627.
+        a = array([[0., 1.], [1., 1.], [2., 1.], [3., 1.]])
+        m, n = a.shape
+        u, s, vh = linalg.svd(a)
+
+        b = dot(transpose(u[:, n:]), a)
+
+        assert_array_almost_equal(b, np.zeros((2, 2)))
+
+    def test_norm_vector_badarg(self):
+        # Regression for #786: Froebenius norm for vectors raises
+        # TypeError.
+        assert_raises(ValueError, linalg.norm, array([1., 2., 3.]), 'fro')
+
+    def test_lapack_endian(self):
+        # For bug #1482
+        a = array([[5.7998084,  -2.1825367],
+                   [-2.1825367,   9.85910595]], dtype='>f8')
+        b = array(a, dtype='<f8')
+
+        ap = linalg.cholesky(a)
+        bp = linalg.cholesky(b)
+        assert_array_equal(ap, bp)
+
+    def test_large_svd_32bit(self):
+        # See gh-4442, 64bit would require very large/slow matrices.
+        x = np.eye(1000, 66)
+        np.linalg.svd(x)
+
+    def test_svd_no_uv(self):
+        # gh-4733
+        for shape in (3, 4), (4, 4), (4, 3):
+            for t in float, complex:
+                a = np.ones(shape, dtype=t)
+                w = linalg.svd(a, compute_uv=False)
+                c = np.count_nonzero(np.absolute(w) > 0.5)
+                assert_equal(c, 1)
+                assert_equal(np.linalg.matrix_rank(a), 1)
+                assert_array_less(1, np.linalg.norm(a, ord=2))
+
+    def test_norm_object_array(self):
+        # gh-7575
+        testvector = np.array([np.array([0, 1]), 0, 0], dtype=object)
+
+        norm = linalg.norm(testvector)
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype == np.dtype('float64'))
+
+        norm = linalg.norm(testvector, ord=1)
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype != np.dtype('float64'))
+
+        norm = linalg.norm(testvector, ord=2)
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype == np.dtype('float64'))
+
+        assert_raises(ValueError, linalg.norm, testvector, ord='fro')
+        assert_raises(ValueError, linalg.norm, testvector, ord='nuc')
+        assert_raises(ValueError, linalg.norm, testvector, ord=np.inf)
+        assert_raises(ValueError, linalg.norm, testvector, ord=-np.inf)
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", DeprecationWarning)
+            assert_raises((AttributeError, DeprecationWarning),
+                              linalg.norm, testvector, ord=0)
+        assert_raises(ValueError, linalg.norm, testvector, ord=-1)
+        assert_raises(ValueError, linalg.norm, testvector, ord=-2)
+
+        testmatrix = np.array([[np.array([0, 1]), 0, 0],
+                               [0,                0, 0]], dtype=object)
+
+        norm = linalg.norm(testmatrix)
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype == np.dtype('float64'))
+
+        norm = linalg.norm(testmatrix, ord='fro')
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype == np.dtype('float64'))
+
+        assert_raises(TypeError, linalg.norm, testmatrix, ord='nuc')
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=np.inf)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=-np.inf)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=0)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=1)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=-1)
+        assert_raises(TypeError, linalg.norm, testmatrix, ord=2)
+        assert_raises(TypeError, linalg.norm, testmatrix, ord=-2)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=3)
+
+    def test_lstsq_complex_larger_rhs(self):
+        # gh-9891
+        size = 20
+        n_rhs = 70
+        G = np.random.randn(size, size) + 1j * np.random.randn(size, size)
+        u = np.random.randn(size, n_rhs) + 1j * np.random.randn(size, n_rhs)
+        b = G.dot(u)
+        # This should work without segmentation fault.
+        u_lstsq, res, rank, sv = linalg.lstsq(G, b, rcond=None)
+        # check results just in case
+        assert_array_almost_equal(u_lstsq, u)