Python pyplot.matshow方法代码示例

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def pearson_filter(projectPath, featuresDf, del_corr_status, del_corr_threshold, del_corr_plot_status):
    print('Reducing features. Correlation threshold: ' + str(del_corr_threshold))
    col_corr = set()
    corr_matrix = featuresDf.corr()
    for i in range(len(corr_matrix.columns)):
        for j in range(i):
            if (corr_matrix.iloc[i, j] >= del_corr_threshold) and (corr_matrix.columns[j] not in col_corr):
                colname = corr_matrix.columns[i]
                col_corr.add(colname)
                if colname in featuresDf.columns:
                    del featuresDf[colname]
    if del_corr_plot_status == 'yes':
        print('Creating feature correlation heatmap...')
        dateTime = datetime.now().strftime('%Y%m%d%H%M%S')
        plt.matshow(featuresDf.corr())
        plt.tight_layout()
        plt.savefig(os.path.join(projectPath, 'logs', 'Feature_correlations_' + dateTime + '.png'), dpi=300)
        plt.close('all')
        print('Feature correlation heatmap .png saved in project_folder/logs directory')

    return featuresDf 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def plotresult(org_vec,noisy_vec,out_vec):
    plt.matshow(np.reshape(org_vec, (28, 28)), cmap=plt.get_cmap('gray'))
    plt.title("Original Image")
    plt.colorbar()

    plt.matshow(np.reshape(noisy_vec, (28, 28)), cmap=plt.get_cmap('gray'))
    plt.title("Input Image")
    plt.colorbar()
    
    outimg = np.reshape(out_vec, (28, 28))
    plt.matshow(outimg, cmap=plt.get_cmap('gray'))
    plt.title("Reconstructed Image")
    plt.colorbar()
    plt.show()

# NETOWRK PARAMETERS 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def plotresult(org_vec,noisy_vec,out_vec):
    plt.matshow(np.reshape(org_vec, (28, 28)), cmap=plt.get_cmap('gray'))
    plt.title("Original Image")
    plt.colorbar()

    plt.matshow(np.reshape(noisy_vec, (28, 28)), cmap=plt.get_cmap('gray'))
    plt.title("Input Image")
    plt.colorbar()
    
    outimg = np.reshape(out_vec, (28, 28))
    plt.matshow(outimg, cmap=plt.get_cmap('gray'))
    plt.title("Reconstructed Image")
    plt.colorbar()
    plt.show()

# NETOWORK PARAMETERS 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def plot_matrix_and_get_image(plot_data, fig_height=8, fig_width=12, axis_off=False, colormap="jet"):
    fig = plt.figure()
    fig.set_figheight(fig_height)
    fig.set_figwidth(fig_width)
    plt.matshow(plot_data, fig.number)

    if fig_height < fig_width:
        plt.colorbar(orientation="horizontal")
    else:
        plt.colorbar(orientation="vertical")

    plt.set_cmap(colormap)
    if axis_off:
        plt.axis('off')

    img = fig_to_img(fig)
    plt.close(fig)
    return img 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def plot_correlation_image(single_one_shot_training_database, epoch_idx=-1):
    correlation_matrix = np.zeros((3, 5))
    for idx_cell, num_cells in enumerate([3, 6, 9]):
        for idx_ch, num_channels in enumerate([2, 4, 8, 16, 36]):
            config = single_one_shot_training_database.query(
                {'unrolled': False, 'cutout': False, 'search_space': '3', 'epochs': 50, 'init_channels': num_channels,
                 'weight_decay': 0.0003, 'warm_start_epochs': 0, 'learning_rate': 0.025, 'layers': num_cells})
            if len(config) > 0:
                correlation = extract_correlation_per_epoch(config)
                correlation_matrix[idx_cell, idx_ch] = 1 - correlation[epoch_idx]

    plt.figure()
    plt.matshow(correlation_matrix)
    plt.xticks(np.arange(5), (2, 4, 8, 16, 36))
    plt.yticks(np.arange(3), (3, 6, 9))
    plt.colorbar()
    plt.savefig('test_correlation.png')
    plt.close()
    return correlation_matrix 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def MyPlot(cwtmatr):
    ''' 绘图 '''
    
    print(type(cwtmatr))
    print(len(cwtmatr))
    print(len(cwtmatr[0]))

    # plt.plot(cwtmatr[1])
    # plt.plot(cwtmatr[10])
    # plt.plot(cwtmatr[100])
    
    plt.plot(cwtmatr[1200])
    plt.plot(cwtmatr[1210])
    plt.plot(cwtmatr[1300])
    plt.plot(cwtmatr[1400])
    plt.plot(cwtmatr[1500])

    # plt.plot(cwtmatr[1800])
    # plt.plot(cwtmatr[1900])
    # plt.plot(cwtmatr[2500])

    # plt.matshow(cwtmatr) 
    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def plot_attention(tokens1, tokens2, attention):
    """
    Print a colormap showing attention values from tokens 1 to
    tokens 2.
    """
    len1 = len(tokens1)
    len2 = len(tokens2)
    extent = [0, len2, 0, len1]
    pl.matshow(attention, extent=extent, aspect='auto')
    ticks1 = np.arange(len1) + 0.5
    ticks2 = np.arange(len2) + 0.5
    pl.xticks(ticks2, tokens2, rotation=45)
    pl.yticks(ticks1, reversed(tokens1))
    ax = pl.gca()
    ax.xaxis.set_ticks_position('bottom')
    pl.colorbar()
    pl.title('Alignments')
    pl.show(block=False) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def render(self, plt_delay=1.0):
        plt.matshow(self.model_state[0].T, cmap=plt.get_cmap('Greys'), fignum=1)
        for i in range(self.pursuer_layer.n_agents()):
            x, y = self.pursuer_layer.get_position(i)
            plt.plot(x, y, "r*", markersize=12)
            if self.train_pursuit:
                ax = plt.gca()
                ofst = self.obs_range / 2.0
                ax.add_patch(
                    Rectangle((x - ofst, y - ofst), self.obs_range, self.obs_range, alpha=0.5,
                              facecolor="#FF9848"))
        for i in range(self.evader_layer.n_agents()):
            x, y = self.evader_layer.get_position(i)
            plt.plot(x, y, "b*", markersize=12)
            if not self.train_pursuit:
                ax = plt.gca()
                ofst = self.obs_range / 2.0
                ax.add_patch(
                    Rectangle((x - ofst, y - ofst), self.obs_range, self.obs_range, alpha=0.5,
                              facecolor="#009ACD"))
        plt.pause(plt_delay)
        plt.clf() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def draw_confusion_matrix(y_test, y_pred):

    from sklearn.metrics import confusion_matrix
    cm = confusion_matrix(y_test, y_pred)
    print(cm)

    # Show confusion matrix in a separate window
    plt.matshow(cm)
    plt.title('Confusion matrix')
    plt.colorbar()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show()


####################10 CV FALSE POSITIVE FLASE NEGATIVe################################################# 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def plotresult(org_vec,noisy_vec,out_vec):
    plt.matshow(np.reshape(org_vec, (28, 28)),\
                cmap=plt.get_cmap('gray'))
    plt.title("Original Image")
    plt.colorbar()

    plt.matshow(np.reshape(noisy_vec, (28, 28)),\
                cmap=plt.get_cmap('gray'))
    plt.title("Input Image")
    plt.colorbar()
    
    outimg   = np.reshape(out_vec, (28, 28))
    plt.matshow(outimg, cmap=plt.get_cmap('gray'))
    plt.title("Reconstructed Image")
    plt.colorbar()
    plt.show()

# NETOWORK PARAMETERS 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def plotresult(org_vec,noisy_vec,out_vec):
    plt.matshow(np.reshape(org_vec, (28, 28)),\
                cmap=plt.get_cmap('gray'))
    plt.title("Original Image")
    plt.colorbar()

    plt.matshow(np.reshape(noisy_vec, (28, 28)),\
                cmap=plt.get_cmap('gray'))
    plt.title("Input Image")
    plt.colorbar()
    
    outimg   = np.reshape(out_vec, (28, 28))
    plt.matshow(outimg, cmap=plt.get_cmap('gray'))
    plt.title("Reconstructed Image")
    plt.colorbar()
    plt.show()

# NETOWRK PARAMETERS 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def confusion_matrix_plot(
        confusion_matrix,
        labels=None,
        output_feature_name=None,
        filename=None
):
    mpl.rcParams.update({'figure.autolayout': True})
    fig, ax = plt.subplots()

    ax.invert_yaxis()
    ax.xaxis.tick_top()
    ax.xaxis.set_label_position('top')

    cax = ax.matshow(confusion_matrix, cmap='viridis')

    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
    ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
    ax.set_xticklabels([''] + labels, rotation=45, ha='left')
    ax.set_yticklabels([''] + labels)
    ax.grid(False)
    ax.tick_params(axis='both', which='both', length=0)
    fig.colorbar(cax, ax=ax, extend='max')
    ax.set_xlabel('Predicted {}'.format(output_feature_name))
    ax.set_ylabel('Actual {}'.format(output_feature_name))

    plt.tight_layout()
    ludwig.contrib.contrib_command("visualize_figure", plt.gcf())
    if filename:
        plt.savefig(filename)
    else:
        plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def plot_matrix(
        matrix,
        cmap='hot',
        filename=None
):
    plt.matshow(matrix, cmap=cmap)
    ludwig.contrib.contrib_command("visualize_figure", plt.gcf())
    if filename:
        plt.savefig(filename)
    else:
        plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def plot_confusion_matrix(cls_pred, data):
    # cls_pred is an array of the predicted class-number for
    # all images in the test-set.

    # Get the true classifications for the test-set.
    cls_true = data.valid.cls
    
    # Get the confusion matrix using sklearn.
    cm = confusion_matrix(y_true=cls_true,
                          y_pred=cls_pred)

    # Print the confusion matrix as text.
    print(cm)

    # Plot the confusion matrix as an image.
    plt.matshow(cm)

    # Make various adjustments to the plot.
    plt.colorbar()
    tick_marks = np.arange(num_classes)
    plt.xticks(tick_marks, range(num_classes))
    plt.yticks(tick_marks, range(num_classes))
    plt.xlabel('Predicted')
    plt.ylabel('True')

    # Ensure the plot is shown correctly with multiple plots
    # in a single Notebook cell.
    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import matshow [as 别名]
def show_alignment(weights, transcription,
                   bos_symbol=False, energies=None,
                   **kwargs):
    f = pyplot.figure(figsize=(15, 0.20 * len(transcription)))
    ax = f.gca()
    ax.matshow(weights, aspect='auto', **kwargs)
    ax.set_yticks((1 if bos_symbol else 0) + numpy.arange(len(transcription)))
    ax.set_yticklabels(transcription)
    pyplot.show()

    if energies is not None:
        pyplot.matshow(energies, **kwargs)
        pyplot.colorbar()
        pyplot.show()