numpy.inner - python examples

Here are the examples of the python api numpy.inner taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.

57 Examples 7

3 View Complete Implementation : copulaLDA.py
Copyright GNU General Public License v3.0
Author : balikasg
    def perplexity(self):
        docs = self.docs
        phi = self.worddist()
        log_per, N = 0, 0
        Kalpha = self.K * self.alpha
        for m, doc in enumerate(docs):
            theta = self.n_m_z[m] / (sum([len(x) for x in doc]) + Kalpha)
            for key, val in enumerate(doc):
                for w in val:
                    log_per -= np.log(np.inner(phi[:,w], theta))
        return np.exp(log_per / self.N)

3 View Complete Implementation : lda.py
Copyright GNU General Public License v3.0
Author : balikasg
    def perplexity(self):
        docs = self.docs
        phi = self.worddist()
        log_per, N = 0, 0
        Kalpha = self.K * self.alpha
        for m, doc in enumerate(docs):
            theta = self.n_m_z[m] / (len(docs[m]) + Kalpha)
            for w in doc:
                log_per -= np.log(np.inner(phi[:,w], theta))
            N += len(doc)
        return np.exp(log_per / N)

3 View Complete Implementation : lda_sentenceLayer.py
Copyright GNU General Public License v3.0
Author : balikasg
    def perplexity(self, docs=None):
        if docs == None: docs = self.docs
        phi = self.worddist()
        log_per = 0
        N = 0
        Kalpha = self.K * self.alpha
        for m, doc in enumerate(docs):
            theta = self.n_m_z[m] / (len(doc) + Kalpha)
            for sen in doc:
                for w in sen:
                    log_per -= np.log(np.inner(phi[:,w], theta))
                N += len(sen)
        return np.exp(log_per / N)

3 View Complete Implementation : recursive_ls.py
Copyright MIT License
Author : birforce
    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 clast, where the params are the final
        # estimates of the regression coefficients
        self['obs_cov', 0, 0] = sigma2
        return self.smooth()

3 View Complete Implementation : imitator.py
Copyright MIT License
Author : CalciferZh
  def compute_rodrigues(self, x, y):
    """
    Compute rotation matrix R such that y = Rx.

    Parameter
    ---------
    x: Ndarray to be rotated.
    y: Ndarray after rotation.

    """
    theta = np.arccos(np.inner(x, y) / (np.linalg.norm(x) * np.linalg.norm(y)))
    axis = np.squeeze(np.cross(x, y))
    return transforms3d.axangles.axangle2mat(axis, theta)

3 View Complete Implementation : sentence.py
Copyright Apache License 2.0
Author : cap-ntu
def plot_similarity(labels, features, rotation):
    # plt.figure()
    corr = np.inner(features, features)
    sns.set(font_scale=1.2)
    g = sns.heatmap(
        corr,
        xticklabels=labels,
        yticklabels=labels,
        vmin=0,
        vmax=1,
        cmap="YlOrRd")
    g.set_xticklabels(labels, rotation=rotation)
    g.set_satle("Semantic Textual Similarity")
    plt.show()

3 View Complete Implementation : N-Charlie.py
Copyright GNU General Public License v3.0
Author : chbpku
def emergency_index(info):  # consider time, distance
    time = np.inner(info[0], info[1]) / (np.inner(info[1], info[1]) + 1e-12)  # <0:approaching, >0:leaving
    dist = np.linalg.norm(info[0] - time * info[1])
    dist -= (info[2] + 1)
    time_index = 0 if time > 0 else 1 - np.tanh(-time / 50)
    dist_index = 1 / (np.exp(dist / 1.5))
    # print(time,time_index)
    return time_index * dist_index

3 View Complete Implementation : N-Charlie.py
Copyright GNU General Public License v3.0
Author : chbpku
def value_index(info):
    time = np.inner(info[0], info[1]) / (np.inner(info[1], info[1]) + 1e-12)  # <0:approaching, >0:leaving
    dist = np.linalg.norm(info[0] - time * info[1])
    dist -= (info[2] + 1)
    time_index =  0 if time < 0 else 1 - np.tanh(-time / 50)
    dist_index = 2 / (1 + np.exp(dist / 5.))
    return time_index * dist_index

3 View Complete Implementation : MeshTweaker.py
Copyright GNU General Public License v3.0
Author : ChristophSchranz
    def project_verteces(self, mesh, orientation):
        """Supplement the mesh array with scalars (max and median)
        for each face projected onto the orientation vector.
        Args:
            mesh (np.array): with format face_count x 6 x 3.
            orientation (np.array): with format 3 x 3.
        Returns:
            adjusted mesh.
        """
        mesh[:, 4, 0] = np.inner(mesh[:, 1, :], orientation)
        mesh[:, 4, 1] = np.inner(mesh[:, 2, :], orientation)
        mesh[:, 4, 2] = np.inner(mesh[:, 3, :], orientation)

        mesh[:, 5, 1] = np.max(mesh[:, 4, :], axis=1)
        mesh[:, 5, 2] = np.median(mesh[:, 4, :], axis=1)
        sleep(0)  # Yield, so other threads get a bit of breathing space.
        return mesh

3 View Complete Implementation : word2vec.py
Copyright MIT License
Author : cod3licious
    def similarity(self, w1, w2):
        """
        Compute cosine similarity between two words.

        Example::
          >>> trained_model.similarity('woman', 'man')
          0.73723527
        """
        return np.inner(self.syn0norm[self.vocab[w1].index], self.syn0norm[self.vocab[w2].index])