Python numpy.inner方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def random_projection(X):

        data_demension = X.shape[1]

        new_data_demension = random.randint(2, data_demension)

        new_X = np.empty((data_demension, new_data_demension))

        minus_one = 0.1
        positive_one = 0.9

        for i in range(len(new_X)):
            for j in range(len(new_X[i])):
                rand = random.random()
                if rand < minus_one:
                    new_X[i][j] = -1.0
                elif rand >= positive_one:
                    new_X[i][j] = 1.0
                else:
                    new_X[i][j] = 0.0

        new_X = np.inner(X, new_X.T)

        return new_X 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def __init__(self, y, x, height, width, angle):
        """
        A efficient tool to rotate multiple images in the same size.
        :author 申瑞瑉 (Ruimin Shen)
        :param y: The y coordinate of rotation point.
        :param x: The x coordinate of rotation point.
        :param height: Image height.
        :param width: Image width.
        :param angle: Rotate angle.
        """
        self._mat = cv2.getRotationMatrix2D((x, y), angle, 1.0)
        r = np.abs(self._mat[0, :2])
        _height, _width = np.inner(r, [height, width]), np.inner(r, [width, height])
        fix_y, fix_x = _height / 2 - y, _width / 2 - x
        self._mat[:, 2] += [fix_x, fix_y]
        self._size = int(_width), int(_height) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def test_4(self):
        """
        Test of take, transpose, inner, outer products.

        """
        x = self.arange(24)
        y = np.arange(24)
        x[5:6] = self.masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert self.allequal(np.transpose(y, (2, 0, 1)), self.transpose(x, (2, 0, 1)))
        assert self.allequal(np.take(y, (2, 0, 1), 1), self.take(x, (2, 0, 1), 1))
        assert self.allequal(np.inner(self.filled(x, 0), self.filled(y, 0)),
                            self.inner(x, y))
        assert self.allequal(np.outer(self.filled(x, 0), self.filled(y, 0)),
                            self.outer(x, y))
        y = self.array(['abc', 1, 'def', 2, 3], object)
        y[2] = self.masked
        t = self.take(y, [0, 3, 4])
        assert t[0] == 'abc'
        assert t[1] == 2
        assert t[2] == 3 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def test_testTakeTransposeInnerOuter(self):
        # Test of take, transpose, inner, outer products
        x = arange(24)
        y = np.arange(24)
        x[5:6] = masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert_(eq(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1))))
        assert_(eq(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1)))
        assert_(eq(np.inner(filled(x, 0), filled(y, 0)),
                   inner(x, y)))
        assert_(eq(np.outer(filled(x, 0), filled(y, 0)),
                   outer(x, y)))
        y = array(['abc', 1, 'def', 2, 3], object)
        y[2] = masked
        t = take(y, [0, 3, 4])
        assert_(t[0] == 'abc')
        assert_(t[1] == 2)
        assert_(t[2] == 3) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def test_einsum_all_contig_non_contig_output(self):
        # Issue gh-5907, tests that the all contiguous special case
        # actually checks the contiguity of the output
        x = np.ones((5, 5))
        out = np.ones(10)[::2]
        correct_base = np.ones(10)
        correct_base[::2] = 5
        # Always worked (inner iteration is done with 0-stride):
        np.einsum('mi,mi,mi->m', x, x, x, out=out)
        assert_array_equal(out.base, correct_base)
        # Example 1:
        out = np.ones(10)[::2]
        np.einsum('im,im,im->m', x, x, x, out=out)
        assert_array_equal(out.base, correct_base)
        # Example 2, buffering causes x to be contiguous but
        # special cases do not catch the operation before:
        out = np.ones((2, 2, 2))[..., 0]
        correct_base = np.ones((2, 2, 2))
        correct_base[..., 0] = 2
        x = np.ones((2, 2), np.float32)
        np.einsum('ij,jk->ik', x, x, out=out)
        assert_array_equal(out.base, correct_base) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def word_sim_test(filename, pos_vectors):
    delim = ','
    actual_sim_list, pred_sim_list = [], []
    missed = 0

    with open(filename, 'r') as pairs:
        for pair in pairs:
            w1, w2, actual_sim = pair.strip().split(delim)

            try:
                w1_vec = create_word_vector(w1, pos_vectors)
                w2_vec = create_word_vector(w2, pos_vectors)
                pred = float(np.inner(w1_vec, w2_vec))
                actual_sim_list.append(float(actual_sim))
                pred_sim_list.append(pred)

            except KeyError:
                missed += 1

    spearman, _ = st.spearmanr(actual_sim_list, pred_sim_list)
    pearson, _ = st.pearsonr(actual_sim_list, pred_sim_list)

    return spearman, pearson, missed 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def test_lanczos_evolve(n, N_cache, tol=5.e-14):
    # generate Hermitian test array
    leg = gen_random_legcharge(ch, n)
    H = npc.Array.from_func_square(rmat.GUE, leg) - npc.diag(1., leg)
    H_flat = H.to_ndarray()
    H_Op = H  # use `matvec` of the array
    qtotal = leg.to_qflat()[0]
    psi_init = npc.Array.from_func(np.random.random, [leg], qtotal=qtotal)
    psi_init /= npc.norm(psi_init)
    psi_init_flat = psi_init.to_ndarray()
    lanc = lanczos.LanczosEvolution(H_Op, psi_init, {'verbose': 1, 'N_cache': N_cache})
    for delta in [-0.1j, 0.1j, 1.j]:  #, 0.1, 1.]:
        psi_final_flat = expm(H_flat * delta).dot(psi_init_flat)
        psi_final, N = lanc.run(delta)
        ov = np.inner(psi_final.to_ndarray().conj(), psi_final_flat)
        ov /= np.linalg.norm(psi_final_flat)
        print("<psi1|psi1_flat>/norm=", ov)
        assert (abs(1. - abs(ov)) < tol) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def test_predict(self):
        X_test = np.random.rand(10, 2)

        m, v = self.model.predict(X_test)

        assert len(m.shape) == 1
        assert m.shape[0] == X_test.shape[0]
        assert len(v.shape) == 1
        assert v.shape[0] == X_test.shape[0]

        m, v = self.model.predict(X_test, full_cov=True)

        assert len(m.shape) == 1
        assert m.shape[0] == X_test.shape[0]
        assert len(v.shape) == 2
        assert v.shape[0] == X_test.shape[0]
        assert v.shape[1] == X_test.shape[0]

        K_zz = self.kernel.get_value(X_test)
        K_zx = self.kernel.get_value(X_test, self.X)
        K_nz = self.kernel.get_value(self.X) + self.model.noise * np.eye(self.X.shape[0])
        inv = spla.inv(K_nz)
        K_zz_x = K_zz - np.dot(K_zx, np.inner(inv, K_zx))
        assert np.mean((K_zz_x - v) ** 2) < 10e-5 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def test_TakeTransposeInnerOuter(self):
        # Test of take, transpose, inner, outer products
        x = arange(24)
        y = np.arange(24)
        x[5:6] = masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert_equal(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1)))
        assert_equal(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1))
        assert_equal(np.inner(filled(x, 0), filled(y, 0)),
                     inner(x, y))
        assert_equal(np.outer(filled(x, 0), filled(y, 0)),
                     outer(x, y))
        y = array(['abc', 1, 'def', 2, 3], object)
        y[2] = masked
        t = take(y, [0, 3, 4])
        assert_(t[0] == 'abc')
        assert_(t[1] == 2)
        assert_(t[2] == 3) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def test_einsum_misc(self):
        # This call used to crash because of a bug in
        # PyArray_AssignZero
        a = np.ones((1, 2))
        b = np.ones((2, 2, 1))
        assert_equal(np.einsum('ij...,j...->i...', a, b), [[[2], [2]]])

        # The iterator had an issue with buffering this reduction
        a = np.ones((5, 12, 4, 2, 3), np.int64)
        b = np.ones((5, 12, 11), np.int64)
        assert_equal(np.einsum('ijklm,ijn,ijn->', a, b, b),
                        np.einsum('ijklm,ijn->', a, b))

        # Issue #2027, was a problem in the contiguous 3-argument
        # inner loop implementation
        a = np.arange(1, 3)
        b = np.arange(1, 5).reshape(2, 2)
        c = np.arange(1, 9).reshape(4, 2)
        assert_equal(np.einsum('x,yx,zx->xzy', a, b, c),
                    [[[1,  3], [3,  9], [5, 15], [7, 21]],
                    [[8, 16], [16, 32], [24, 48], [32, 64]]]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def fit(self):
        """
        Fits the model by application of the Kalman filter

        Returns
        -------
        RecursiveLSResults
        """
        # Get the smoother results with an arbitrary measurement variance
        smoother_results = self.smooth(return_ssm=True)
        # Compute the MLE of sigma2 (see Harvey, 1989 equation 4.2.5)
        resid = smoother_results.standardized_forecasts_error[0]
        sigma2 = (np.inner(resid, resid) /
                  (self.nobs - self.loglikelihood_burn))

        # Now construct a results class, where the params are the final
        # estimates of the regression coefficients
        self['obs_cov', 0, 0] = sigma2
        return self.smooth() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def test_4(self):
        "Test of take, transpose, inner, outer products"
        x = self.arange(24)
        y = np.arange(24)
        x[5:6] = self.masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert self.allequal(np.transpose(y, (2, 0, 1)), self.transpose(x, (2, 0, 1)))
        assert self.allequal(np.take(y, (2, 0, 1), 1), self.take(x, (2, 0, 1), 1))
        assert self.allequal(np.inner(self.filled(x, 0), self.filled(y, 0)),
                            self.inner(x, y))
        assert self.allequal(np.outer(self.filled(x, 0), self.filled(y, 0)),
                            self.outer(x, y))
        y = self.array(['abc', 1, 'def', 2, 3], object)
        y[2] = self.masked
        t = self.take(y, [0, 3, 4])
        assert t[0] == 'abc'
        assert t[1] == 2
        assert t[2] == 3
    #---------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def test_testTakeTransposeInnerOuter(self):
        "Test of take, transpose, inner, outer products"
        x = arange(24)
        y = np.arange(24)
        x[5:6] = masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert_(eq(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1))))
        assert_(eq(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1)))
        assert_(eq(np.inner(filled(x, 0), filled(y, 0)),
                                inner(x, y)))
        assert_(eq(np.outer(filled(x, 0), filled(y, 0)),
                                outer(x, y)))
        y = array(['abc', 1, 'def', 2, 3], object)
        y[2] = masked
        t = take(y, [0, 3, 4])
        assert_(t[0] == 'abc')
        assert_(t[1] == 2)
        assert_(t[2] == 3) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def test_TakeTransposeInnerOuter(self):
        "Test of take, transpose, inner, outer products"
        x = arange(24)
        y = np.arange(24)
        x[5:6] = masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert_equal(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1)))
        assert_equal(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1))
        assert_equal(np.inner(filled(x, 0), filled(y, 0)),
                            inner(x, y))
        assert_equal(np.outer(filled(x, 0), filled(y, 0)),
                            outer(x, y))
        y = array(['abc', 1, 'def', 2, 3], object)
        y[2] = masked
        t = take(y, [0, 3, 4])
        assert_(t[0] == 'abc')
        assert_(t[1] == 2)
        assert_(t[2] == 3) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import inner [as 別名]
def main(args_dict):
    text_embedder = FakeTextEmbedder()
    text_embedder.embed(["How are you?", "What is the time?", "What time it is?"])

    texts = ["Regenerácia vnútroblokov sídlisk mesta Brezno",
             "Územný plán mesta Brezno",
             "Oprava miestnych komunikácií v katastrálnom území mesta Brezno",
             "Kompostéry pre obec Kamenec pod Vtáčnikom",
             "Most cez potok Kamenec , Bardejov - mestská časť Dlhá Lúka",
             "Oprava miestnych komunikácií v katastrálnom území Trencin"]
    word2vec_embedder = Word2VecEmbedder(texts)
    embeddings = word2vec_embedder.embed(texts)
    print('Similarities:')
    for emb1, text1 in zip(embeddings, texts):
        for emb2, text2 in zip(embeddings, texts):
            similarity = np.inner(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2))
            print(similarity, text1, ' ----', text2)