numpy.random.randint - python examples

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

145 Examples 7

3 View Complete Implementation : sequence.py
Copyright MIT License
Author : hello-sea
    def __gesatem__(self, index):
        if self.shuffle:
            rows = np.random.randint(
                self.start_index, self.end_index, size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size *
                                    self.stride, self.end_index), self.stride)

        samples, targets = self._empty_batch(len(rows))
        for j, row in enumerate(rows):
            indices = range(rows[j] - self.length, rows[j], self.sampling_rate)
            samples[j] = self.data[indices]
            targets[j] = self.targets[rows[j]]
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

3 View Complete Implementation : sequence.py
Copyright MIT License
Author : hello-sea
    def __gesatem__(self, index):
        if self.shuffle:
            rows = np.random.randint(
                self.start_index, self.end_index, size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size *
                                    self.stride, self.end_index), self.stride)

        samples, targets = self._empty_batch(len(rows))
        for j, row in enumerate(rows):
            indices = range(rows[j] - self.length, rows[j], self.sampling_rate)
            samples[j] = self.data[indices]
            targets[j] = self.targets[rows[j]]
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

3 View Complete Implementation : sequence.py
Copyright MIT License
Author : hello-sea
    def __gesatem__(self, index):
        if self.shuffle:
            rows = np.random.randint(
                self.start_index, self.end_index, size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size *
                                    self.stride, self.end_index), self.stride)

        samples, targets = self._empty_batch(len(rows))
        for j, row in enumerate(rows):
            indices = range(rows[j] - self.length, rows[j], self.sampling_rate)
            samples[j] = self.data[indices]
            targets[j] = self.targets[rows[j]]
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

3 View Complete Implementation : sequence.py
Copyright MIT License
Author : hello-sea
    def __gesatem__(self, index):
        if self.shuffle:
            rows = np.random.randint(
                self.start_index, self.end_index, size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size *
                                    self.stride, self.end_index), self.stride)

        samples, targets = self._empty_batch(len(rows))
        for j, row in enumerate(rows):
            indices = range(rows[j] - self.length, rows[j], self.sampling_rate)
            samples[j] = self.data[indices]
            targets[j] = self.targets[rows[j]]
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

3 View Complete Implementation : sequence.py
Copyright MIT License
Author : hello-sea
    def __gesatem__(self, index):
        if self.shuffle:
            rows = np.random.randint(
                self.start_index, self.end_index, size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size *
                                    self.stride, self.end_index), self.stride)

        samples, targets = self._empty_batch(len(rows))
        for j, row in enumerate(rows):
            indices = range(rows[j] - self.length, rows[j], self.sampling_rate)
            samples[j] = self.data[indices]
            targets[j] = self.targets[rows[j]]
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

3 View Complete Implementation : sequence.py
Copyright MIT License
Author : hello-sea
    def __gesatem__(self, index):
        if self.shuffle:
            rows = np.random.randint(
                self.start_index, self.end_index, size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size *
                                    self.stride, self.end_index), self.stride)

        samples, targets = self._empty_batch(len(rows))
        for j, row in enumerate(rows):
            indices = range(rows[j] - self.length, rows[j], self.sampling_rate)
            samples[j] = self.data[indices]
            targets[j] = self.targets[rows[j]]
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

3 View Complete Implementation : sequence.py
Copyright MIT License
Author : hello-sea
    def __gesatem__(self, index):
        if self.shuffle:
            rows = np.random.randint(
                self.start_index, self.end_index, size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size *
                                    self.stride, self.end_index), self.stride)

        samples, targets = self._empty_batch(len(rows))
        for j, row in enumerate(rows):
            indices = range(rows[j] - self.length, rows[j], self.sampling_rate)
            samples[j] = self.data[indices]
            targets[j] = self.targets[rows[j]]
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

3 View Complete Implementation : sequence.py
Copyright MIT License
Author : hello-sea
    def __gesatem__(self, index):
        if self.shuffle:
            rows = np.random.randint(
                self.start_index, self.end_index, size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size *
                                    self.stride, self.end_index), self.stride)

        samples, targets = self._empty_batch(len(rows))
        for j, row in enumerate(rows):
            indices = range(rows[j] - self.length, rows[j], self.sampling_rate)
            samples[j] = self.data[indices]
            targets[j] = self.targets[rows[j]]
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

3 View Complete Implementation : sequence.py
Copyright MIT License
Author : hello-sea
    def __gesatem__(self, index):
        if self.shuffle:
            rows = np.random.randint(
                self.start_index, self.end_index, size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size *
                                    self.stride, self.end_index), self.stride)

        samples, targets = self._empty_batch(len(rows))
        for j, row in enumerate(rows):
            indices = range(rows[j] - self.length, rows[j], self.sampling_rate)
            samples[j] = self.data[indices]
            targets[j] = self.targets[rows[j]]
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

3 View Complete Implementation : sequence.py
Copyright MIT License
Author : hello-sea
    def __gesatem__(self, index):
        if self.shuffle:
            rows = np.random.randint(
                self.start_index, self.end_index, size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size *
                                    self.stride, self.end_index), self.stride)

        samples, targets = self._empty_batch(len(rows))
        for j, row in enumerate(rows):
            indices = range(rows[j] - self.length, rows[j], self.sampling_rate)
            samples[j] = self.data[indices]
            targets[j] = self.targets[rows[j]]
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets