def affine_backward(dout, cache): """ Computes the backward pass for an affine layer. Inputs: - dout: Upstream derivative, of shape (N, M) - cache: Tuple of: - x: Input data, of shape (N, d_1, ... d_k) - w: Weights, of shape (D, M) Returns a tuple of: - dx: Gradient with respect to x, of shape (N, d1, ..., d_k) - dw: Gradient with respect to w, of shape (D, M) - db: Gradient with respect to b, of shape (M,) """ x, w, b = cache x_plain = np.reshape(x, (x.shape[0], -1)) db = np.sum(dout, axis=0) dx_plain = np.dot(dout, np.transpose(w)) dx = np.reshape(dx_plain, x.shape) dw = np.dot(np.transpose(x_plain), dout) return dx, dw, db
def forward(self, inputs, params): N, C, W, H = inputs.shape inputs = transpose(inputs, (0, 2, 3, 1)) inputs = np.reshape(inputs, (N * W * H, C)) outputs, running_mean, running_variance = layers.batchnorm( inputs, params[self.gamma], params[self.beta], params['__training_in_progress__'], self.epsilon, self.momentum, self.running_mean, self.running_variance) self.running_mean, self.running_variance = running_mean, running_variance outputs = np.reshape(outputs, (N, W, H, C)) outputs = transpose(outputs, (0, 3, 1, 2)) return outputs
def affine_forward(x, w, b): """ Computes the forward pass for an affine (fully-connected) layer. The input x has shape (N, d_1, ..., d_k) and contains a minibatch of N examples, where each example x[i] has shape (d_1, ..., d_k). We will reshape each input into a vector of dimension D = d_1 * ... * d_k, and then transform it to an output vector of dimension M. Inputs: - x: A numpy array containing input data, of shape (N, d_1, ..., d_k) - w: A numpy array of weights, of shape (D, M) - b: A numpy array of biases, of shape (M,) Returns a tuple of: - out: output, of shape (N, M) - cache: (x, w, b) """ x_plain = np.reshape(x, (x.shape[0], -1)) out = np.dot(x_plain, w) + b cache = (x, w, b) return out, cache
def test_index_slice(): a = np.arange(6) a = np.reshape(a, (3, 2)) print(a[:]) a[1:2] = -1 print(a) d = np.slice_axis(a, axis=1, begin=1, end=2) print(d)
def blob_normalization(X, settings, gamma, beta, mode='train', epsilon=1e-5, momentum=0.9, running_mean=None, running_variance=None): N, D = map(int, X.shape) size = N * D if running_mean is None: running_mean = np.zeros(1) if running_variance is None: running_variance = np.zeros(1) if mode == 'train': if 'shared_mean' in settings: mean = np.sum(X) / size else: mean = np.sum(X, axis=0) / N mean = np.reshape(mean, (1, D)) centered_X = X - mean if 'shared_deviation' in settings: variance = np.sum(centered_X**2) / size else: variance = np.sum(centered_X**2, axis=0) / N variance = np.reshape(variance, (1, D)) deviation = variance**0.5 rescaled_X = centered_X / deviation out = gamma * rescaled_X + beta running_mean = momentum * running_mean + (1.0 - momentum) * mean running_variance = momentum * running_variance + (1.0 - momentum) * variance elif mode == 'test': X_hat = (X - running_mean) / np.sqrt(running_variance + epsilon) out = gamma * X_hat + beta return out, running_mean, running_variance
def loss_and_derivative(self, X, y=None): """ Compute loss and gradient for the fully-connected net. Input / output: Same as TwoLayerNet above. """ X_plain = np.reshape(X, (X.shape[0], -1)) mode = 'test' if y is None else 'train' if self.dropout_param is not None: self.dropout_param['mode'] = mode if self.use_batchnorm: for bn_param in self.bn_params: bn_param['mode'] = mode params_array = self.pack_params() def train_loss(*args): X = args[0] y = args[1] res = X for l in xrange(self.num_layers): prev_res = res res = affine_forward(prev_res, args[self.w_idx(l)], args[self.b_idx(l)]) if l < (self.num_layers - 1): if self.use_batchnorm: res = batchnorm_forward(res, args[self.bn_ga_idx(l)], args[self.bn_bt_idx(l)], self.bn_params[l]) res = relu_forward(res) if self.use_dropout: res = dropout_forward(res, self.dropout_param) scores = res if mode == 'test': return scores #loss, _ = softmax_loss(scores, y) loss = svm_loss(scores, y) return loss if y is None: return train_loss(X_plain, y, *params_array) grad_function = grad_and_loss(train_loss, range(self.data_target_cnt, self.data_target_cnt + len(params_array))) grads_array, loss = grad_function(X_plain, y, *params_array) grads = {} for i, grad in enumerate(grads_array): grads[self.param_keys[i]] = grad return loss, grads
def forward(self, X, mode): # Flatten the input data to matrix. X = np.reshape(X, (batch_size, flattened_input_size)) # First affine layer (fully-connected layer). y1 = layers.affine(X, self.params['w1'], self.params['b1']) # ReLU activation. y2 = layers.relu(y1) # Second affine layer. y3 = layers.affine(y2, self.params['w2'], self.params['b2']) return y3
def loss_and_derivative(self, X, y=None): """ Compute loss and gradient for a minibatch of data. Inputs: - X: Array of input data of shape (N, d_1, ..., d_k) - y: Array of labels, of shape (N,). y[i] gives the label for X[i]. Returns: If y is None, then run a test-time forward pass of the model and return: - scores: Array of shape (N, C) giving classification scores, where scores[i, c] is the classification score for X[i] and class c. If y is not None, then run a training-time forward and backward pass and return a tuple of: - loss: Scalar value giving the loss - grads: Dictionary with the same keys as self.params, mapping parameter names to gradients of the loss with respect to those parameters. """ # Note: types of X, y are mxnet.ndarray def train_loss(X, y, W1, W2, b1, b2): l1 = affine_relu_forward(X, W1, b1) l2 = affine_forward(l1, W2, b2) scores = l2 if y is None: return scores #[TODO]: softmax is not supported yet # loss, d_scores = softmax_loss(scores, y) loss = svm_loss(scores, y) loss_with_reg = loss + np.sum(W1**2) * 0.5 * self.reg + np.sum( W2**2) * 0.5 * self.reg return loss_with_reg self.params_array = [] params_list_name = ['W1', 'W2', 'b1', 'b2'] for param_name in params_list_name: self.params_array.append(self.params[param_name]) X_plain = np.reshape(X, (X.shape[0], -1)) if y is None: return train_loss(X_plain, y, *self.params_array) grad_function = grad_and_loss(train_loss, range(2, 6)) grads_array, loss = grad_function(X_plain, y, *self.params_array) grads = {} for i in range(len(params_list_name)): grads[params_list_name[i]] = grads_array[i] return loss, grads
def loss_and_derivative(self, X, y=None): """ Compute loss and gradient for a minibatch of data. Inputs: - X: Array of input data of shape (N, d_1, ..., d_k) - y: Array of labels, of shape (N,). y[i] gives the label for X[i]. Returns: If y is None, then run a test-time forward pass of the model and return: - scores: Array of shape (N, C) giving classification scores, where scores[i, c] is the classification score for X[i] and class c. If y is not None, then run a training-time forward and backward pass and return a tuple of: - loss: Scalar value giving the loss - grads: Dictionary with the same keys as self.params, mapping parameter names to gradients of the loss with respect to those parameters. """ # Note: types of X, y are mxnet.ndarray def train_loss(X, y, W1, W2, b1, b2): l1 = affine_relu_forward(X, W1, b1) l2 = affine_forward(l1, W2, b2) scores = l2 if y is None: return scores #[TODO]: softmax is not supported yet # loss, d_scores = softmax_loss(scores, y) loss = svm_loss(scores, y) loss_with_reg = loss + np.sum(W1 ** 2) * 0.5 * self.reg + np.sum(W2 ** 2) * 0.5 * self.reg return loss_with_reg self.params_array = [] params_list_name = ['W1', 'W2', 'b1', 'b2'] for param_name in params_list_name: self.params_array.append(self.params[param_name]) X_plain = np.reshape(X, (X.shape[0], -1)) if y is None: return train_loss(X_plain, y, *self.params_array) grad_function = grad_and_loss(train_loss, range(2, 6)) grads_array, loss = grad_function(X_plain, y, *self.params_array) grads = {} for i in range(len(params_list_name)): grads[params_list_name[i]] = grads_array[i] return loss, grads
def forward(self, X, mode): # Flatten the input data to matrix. X = np.reshape(X, (self.batch_size, 784)) # First affine layer (fully-connected layer). y1 = layers.affine(X, self.params['wi'], self.params['bi']) # ReLU activation. y2 = layers.relu(y1) # Hidden layers. for i in range(self.num_hidden - 1): y2 = layers.affine(y2, self.params['w%d' % i], self.params['b%d' % i]) y2 = layers.relu(y2) # Second affine layer. y3 = layers.affine(y2, self.params['wo'], self.params['bo']) return y3
def forward(self, X, mode): # Flatten the input data to matrix. X = np.reshape(X, (batch_size, 3 * 32 * 32)) # First affine layer (fully-connected layer). y1 = layers.affine(X, self.params['w1'], self.params['b1']) # ReLU activation. y2 = layers.relu(y1) # Batch normalization y3, self.aux_params['running_mean'], self.aux_params['running_var'] = layers.batchnorm( y2, self.params['gamma'], self.params['beta'], running_mean=self.aux_params['running_mean'], \ running_var=self.aux_params['running_var']) # Second affine layer. y4 = layers.affine(y3, self.params['w2'], self.params['b2']) # Dropout y5 = layers.dropout(y4, 0.5, mode=mode) return y5
def test_sum_forward(): np_x = py_np.zeros((2, 10)) np_w = py_np.zeros((10, 3)) np_b = py_np.zeros(3) x = NumpyVarToMinpy(np_x) w = NumpyVarToMinpy(np_w) b = NumpyVarToMinpy(np_b) x_plain = np.reshape(x, (x.shape[0], -1)) out0 = np.dot(x_plain, w) out = out0 + b np_out = MinpyVarToNumpy(out) var = py_np.random.randn(2, 3) tmp = NumpyVarToMinpy(var) sum_tmp = np.sum(tmp, axis=0) sum_py = MinpyVarToNumpy(sum_tmp)
def test_sum_forward(): np_x = py_np.zeros((2, 10)) np_w = py_np.zeros((10, 3)) np_b = py_np.zeros(3) x = NumpyVarToMinpy(np_x) w = NumpyVarToMinpy(np_w) b = NumpyVarToMinpy(np_b) x_plain = np.reshape(x, (x.shape[0], -1)) out0 = np.dot(x_plain, w) out = out0 + b np_out = MinpyVarToNumpy(out) var = py_np.random.randn(2, 3) tmp = NumpyVarToMinpy(var) sum_tmp = np.sum(tmp, axis = 0) sum_py = MinpyVarToNumpy(sum_tmp)
def forward(self, inputs, *args): shape = (inputs.shape[0], int(np.prod(np.array(inputs.shape[1:])))) return np.reshape(inputs, shape)
def loss(w, x): prob = predict(w, x) return np.reshape(-np.sum(np.log(prob) * t) / 10000, (1, 1)) + 0.5 * w * w
def test_numeric(): # 'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc', # 'arange', 'array', 'zeros', 'count_nonzero', 'empty', 'broadcast', # 'dtype', 'fromstring', 'fromfile', 'frombuffer', 'int_asbuffer', # 'where', 'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose', # 'lexsort', 'set_numeric_ops', 'can_cast', 'promote_types', # 'min_scalar_type', 'result_type', 'asarray', 'asanyarray', # 'ascontiguousarray', 'asfortranarray', 'isfortran', 'empty_like', # 'zeros_like', 'ones_like', 'correlate', 'convolve', 'inner', 'dot', # 'einsum', 'outer', 'vdot', 'alterdot', 'restoredot', 'roll', # 'rollaxis', 'moveaxis', 'cross', 'tensordot', 'array2string', # 'get_printoptions', 'set_printoptions', 'array_repr', 'array_str', # 'set_string_function', 'little_endian', 'require', 'fromiter', # 'array_equal', 'array_equiv', 'indices', 'fromfunction', 'isclose', 'load', # 'loads', 'isscalar', 'binary_repr', 'base_repr', 'ones', 'identity', # 'allclose', 'compare_chararrays', 'putmask', 'seterr', 'geterr', # 'setbufsize', 'getbufsize', 'seterrcall', 'geterrcall', 'errstate', # 'flatnonzero', 'Inf', 'inf', 'infty', 'Infinity', 'nan', 'NaN', 'False_', # 'True_', 'bitwise_not', 'full', 'full_like', 'matmul' x = np.arange(6) x = x.reshape((2, 3)) np.zeros_like(x) y = np.arange(3, dtype=np.float) np.zeros_like(y) np.ones(5) np.ones((5,), dtype=np.int) np.ones((2, 1)) s = (2,2) np.ones(s) x = np.arange(6) x = x.reshape((2, 3)) np.ones_like(x) y = np.arange(3, dtype=np.float) np.ones_like(y) np.full((2, 2), np.inf) x = np.arange(6, dtype=np.int) np.full_like(x, 1) np.full_like(x, 0.1) np.full_like(y, 0.1) np.count_nonzero(np.eye(4)) np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]]) np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]], axis=0) np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]], axis=1) a = [1, 2] np.asarray(a) a = np.array([1, 2]) np.asarray(a) is a a = np.array([1, 2], dtype=np.float32) np.asarray(a, dtype=np.float32) is a np.asarray(a, dtype=np.float64) is a np.asarray(a) is a np.asanyarray(a) is a a = [1, 2] np.asanyarray(a) np.asanyarray(a) is a x = np.arange(6).reshape(2,3) np.ascontiguousarray(x, dtype=np.float32) x = np.arange(6).reshape(2,3) y = np.asfortranarray(x) x = np.arange(6).reshape(2,3) y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F']) a = np.array([[1, 2, 3], [4, 5, 6]], order='C') np.isfortran(a) b = np.array([[1, 2, 3], [4, 5, 6]], order='FORTRAN') np.isfortran(b) a = np.array([[1, 2, 3], [4, 5, 6]], order='C') np.isfortran(a) b = a.T np.isfortran(b) np.isfortran(np.array([1, 2], order='FORTRAN')) x = np.arange(6).reshape(2,3) np.argwhere(x>1) x = np.arange(-2, 3) np.flatnonzero(x) np.correlate([1, 2, 3], [0, 1, 0.5]) np.correlate([1, 2, 3], [0, 1, 0.5], "same") np.correlate([1, 2, 3], [0, 1, 0.5], "full") np.correlate([1+1j, 2, 3-1j], [0, 1, 0.5j], 'full') np.correlate([0, 1, 0.5j], [1+1j, 2, 3-1j], 'full') np.convolve([1, 2, 3], [0, 1, 0.5]) np.convolve([1,2,3],[0,1,0.5], 'same') np.convolve([1,2,3],[0,1,0.5], 'valid') rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5)) # im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,))) # grid = rl + im x = np.array(['a', 'b', 'c'], dtype=object) np.outer(x, [1, 2, 3]) a = np.arange(60.).reshape(3,4,5) b = np.arange(24.).reshape(4,3,2) c = np.tensordot(a,b, axes=([1,0],[0,1])) c.shape # A slower but equivalent way of computing the same... d = np.zeros((5,2)) a = np.array(range(1, 9)) A = np.array(('a', 'b', 'c', 'd'), dtype=object) x = np.arange(10) np.roll(x, 2) x2 = np.reshape(x, (2,5)) np.roll(x2, 1) np.roll(x2, 1, axis=0) np.roll(x2, 1, axis=1) a = np.ones((3,4,5,6)) np.rollaxis(a, 3, 1).shape np.rollaxis(a, 2).shape np.rollaxis(a, 1, 4).shape x = np.zeros((3, 4, 5)) np.moveaxis(x, 0, -1).shape np.moveaxis(x, -1, 0).shape np.transpose(x).shape np.moveaxis(x, [0, 1], [-1, -2]).shape np.moveaxis(x, [0, 1, 2], [-1, -2, -3]).shape x = [1, 2, 3] y = [4, 5, 6] np.cross(x, y) x = [1, 2] y = [4, 5, 6] np.cross(x, y) x = [1, 2, 0] y = [4, 5, 6] np.cross(x, y) x = [1,2] y = [4,5] np.cross(x, y) x = np.array([[1,2,3], [4,5,6]]) y = np.array([[4,5,6], [1,2,3]]) np.cross(x, y) np.cross(x, y, axisc=0) x = np.array([[1,2,3], [4,5,6], [7, 8, 9]]) y = np.array([[7, 8, 9], [4,5,6], [1,2,3]]) np.cross(x, y) np.cross(x, y, axisa=0, axisb=0) # np.array_repr(np.array([1,2])) # np.array_repr(np.ma.array([0.])) # np.array_repr(np.array([], np.int32)) x = np.array([1e-6, 4e-7, 2, 3]) # np.array_repr(x, precision=6, suppress_small=True) # np.array_str(np.arange(3)) a = np.arange(10) x = np.arange(4) np.set_string_function(lambda x:'random', repr=False) grid = np.indices((2, 3)) grid.shape grid[0] # row indices grid[1] # column indices x = np.arange(20).reshape(5, 4) row, col = np.indices((2, 3)) x[row, col] np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int) np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int) np.isscalar(3.1) np.isscalar([3.1]) np.isscalar(False) # np.binary_repr(3) # np.binary_repr(-3) # np.binary_repr(3, width=4) # np.binary_repr(-3, width=3) # np.binary_repr(-3, width=5) # np.base_repr(5) # np.base_repr(6, 5) # np.base_repr(7, base=5, padding=3) # np.base_repr(10, base=16) # np.base_repr(32, base=16) np.identity(3) np.allclose([1e10,1e-7], [1.00001e10,1e-8]) np.allclose([1e10,1e-8], [1.00001e10,1e-9]) np.allclose([1e10,1e-8], [1.0001e10,1e-9]) # np.allclose([1.0, np.nan], [1.0, np.nan]) # np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True) np.isclose([1e10,1e-7], [1.00001e10,1e-8]) np.isclose([1e10,1e-8], [1.00001e10,1e-9]) np.isclose([1e10,1e-8], [1.0001e10,1e-9]) # np.isclose([1.0, np.nan], [1.0, np.nan]) # np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True) np.array_equal([1, 2], [1, 2]) np.array_equal(np.array([1, 2]), np.array([1, 2])) np.array_equal([1, 2], [1, 2, 3]) np.array_equal([1, 2], [1, 4]) np.array_equiv([1, 2], [1, 2]) np.array_equiv([1, 2], [1, 3]) np.array_equiv([1, 2], [[1, 2], [1, 2]]) np.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]]) np.array_equiv([1, 2], [[1, 2], [1, 3]])
def loss_and_derivative(self, X, y=None): """ Compute loss and gradient for the fully-connected net. Input / output: Same as TwoLayerNet above. """ X_plain = np.reshape(X, (X.shape[0], -1)) mode = 'test' if y is None else 'train' if self.dropout_param is not None: self.dropout_param['mode'] = mode if self.use_batchnorm: for bn_param in self.bn_params: bn_param['mode'] = mode params_array = self.pack_params() def train_loss(*args): X = args[0] y = args[1] res = X for l in xrange(self.num_layers): prev_res = res res = affine_forward(prev_res, args[self.w_idx(l)], args[self.b_idx(l)]) if l < (self.num_layers - 1): if self.use_batchnorm: res = batchnorm_forward(res, args[self.bn_ga_idx(l)], args[self.bn_bt_idx(l)], self.bn_params[l]) res = relu_forward(res) if self.use_dropout: res = dropout_forward(res, self.dropout_param) scores = res if mode == 'test': return scores #loss, _ = softmax_loss(scores, y) loss = svm_loss(scores, y) return loss if y is None: return train_loss(X_plain, y, *params_array) grad_function = grad_and_loss( train_loss, range(self.data_target_cnt, self.data_target_cnt + len(params_array))) grads_array, loss = grad_function(X_plain, y, *params_array) grads = {} for i, grad in enumerate(grads_array): grads[self.param_keys[i]] = grad return loss, grads
def test_fromnumeric(): # Functions # 'alen', 'all', 'alltrue', 'amax', 'amin', 'any', 'argmax', # 'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip', # 'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean', # 'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put', # 'rank', 'ravel', 'repeat', 'reshape', 'resize', 'round_', # 'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze', # 'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var', a = [4, 3, 5, 7, 6, 8] indices = [0, 1, 4] np.take(a, indices) a = np.array(a) # a[indices] np.take(a, [[0, 1], [2, 3]]) a = np.zeros((10, 2)) b = a.T a = np.arange(6).reshape((3, 2)) np.reshape(a, (2, 3)) # C-like index ordering np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape np.reshape(a, (2, 3), order='F') # Fortran-like index ordering np.reshape(np.ravel(a, order='F'), (2, 3), order='F') a = np.array([[1,2,3], [4,5,6]]) np.reshape(a, 6) np.reshape(a, 6, order='F') np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2 choices = [[0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33]] np.choose([2, 3, 1, 0], choices) np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1) np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4) a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]] choices = [-10, 10] np.choose(a, choices) a = np.array([0, 1]).reshape((2,1,1)) c1 = np.array([1, 2, 3]).reshape((1,3,1)) c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5)) np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2 np.repeat(3, 4) x = np.array([[1,2],[3,4]]) np.repeat(x, 2) np.repeat(x, 3, axis=1) np.repeat(x, [1, 2], axis=0) a = np.arange(5) np.put(a, [0, 2], [-44, -55]) a = np.arange(5) np.put(a, 22, -5, mode='clip') x = np.array([[1,2,3]]) np.swapaxes(x,0,1) x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]]) np.swapaxes(x,0,2) x = np.arange(4).reshape((2,2)) np.transpose(x) x = np.ones((1, 2, 3)) np.transpose(x, (1, 0, 2)).shape a = np.array([3, 4, 2, 1]) np.partition(a, 3) np.partition(a, (1, 3)) x = np.array([3, 4, 2, 1]) x[np.argpartition(x, 3)] x[np.argpartition(x, (1, 3))] x = [3, 4, 2, 1] np.array(x)[np.argpartition(x, 3)] a = np.array([[1,4],[3,1]]) np.sort(a) # sort along the last axis np.sort(a, axis=None) # sort the flattened array np.sort(a, axis=0) # sort along the first axis dtype = [('name', 'S10'), ('height', float), ('age', int)] values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38), ('Galahad', 1.7, 38)] a = np.array(values, dtype=dtype) # create a structured array np.sort(a, order='height') # doctest: +SKIP np.sort(a, order=['age', 'height']) # doctest: +SKIP x = np.array([3, 1, 2]) np.argsort(x) x = np.array([[0, 3], [2, 2]]) np.argsort(x, axis=0) np.argsort(x, axis=1) x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')]) np.argsort(x, order=('x','y')) np.argsort(x, order=('y','x')) a = np.arange(6).reshape(2,3) np.argmax(a) np.argmax(a, axis=0) np.argmax(a, axis=1) b = np.arange(6) b[1] = 5 np.argmax(b) # Only the first occurrence is returned. a = np.arange(6).reshape(2,3) np.argmin(a) np.argmin(a, axis=0) np.argmin(a, axis=1) b = np.arange(6) b[4] = 0 np.argmin(b) # Only the first occurrence is returned. np.searchsorted([1,2,3,4,5], 3) np.searchsorted([1,2,3,4,5], 3, side='right') np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3]) a=np.array([[0,1],[2,3]]) np.resize(a,(2,3)) np.resize(a,(1,4)) np.resize(a,(2,4)) x = np.array([[[0], [1], [2]]]) x.shape np.squeeze(x).shape np.squeeze(x, axis=(2,)).shape a = np.arange(4).reshape(2,2) a = np.arange(8).reshape(2,2,2); a a[:,:,0] # main diagonal is [0 6] a[:,:,1] # main diagonal is [1 7] np.trace(np.eye(3)) a = np.arange(8).reshape((2,2,2)) np.trace(a) a = np.arange(24).reshape((2,2,2,3)) np.trace(a).shape x = np.array([[1, 2, 3], [4, 5, 6]]) np.ravel(x) x.reshape(-1) np.ravel(x, order='F') np.ravel(x.T) np.ravel(x.T, order='A') a = np.arange(3)[::-1]; a # a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a x = np.eye(3) np.nonzero(x) x[np.nonzero(x)] np.transpose(np.nonzero(x)) a = np.array([[1,2,3],[4,5,6],[7,8,9]]) a > 3 np.nonzero(a > 3) np.shape(np.eye(3)) np.shape([[1, 2]]) np.shape([0]) np.shape(0) a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')]) np.shape(a) a.shape a = np.array([[1, 2], [3, 4], [5, 6]]) np.compress([0, 1], a, axis=0) np.compress([False, True, True], a, axis=0) np.compress([False, True], a, axis=1) np.compress([False, True], a) a = np.arange(10) np.clip(a, 1, 8) np.clip(a, 3, 6, out=a) a = np.arange(10) np.clip(a, [3,4,1,1,1,4,4,4,4,4], 8) np.sum([]) np.sum([0.5, 1.5]) np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32) np.sum([[0, 1], [0, 5]]) np.sum([[0, 1], [0, 5]], axis=0) np.sum([[0, 1], [0, 5]], axis=1) # np.ones(128, dtype=np.int8).sum(dtype=np.int8) # np.any([[True, False], [True, True]]) # np.any([[True, False], [False, False]], axis=0) # np.any([-1, 0, 5]) # np.any(np.nan) # np.all([[True,False],[True,True]]) # np.all([[True,False],[True,True]], axis=0) # np.all([-1, 4, 5]) # np.all([1.0, np.nan]) a = np.array([[1,2,3], [4,5,6]]) np.cumsum(a) np.cumsum(a, dtype=float) # specifies type of output value(s) np.cumsum(a,axis=0) # sum over rows for each of the 3 columns np.cumsum(a,axis=1) # sum over columns for each of the 2 rows x = np.arange(4).reshape((2,2)) np.ptp(x, axis=0) np.ptp(x, axis=1) a = np.arange(4).reshape((2,2)) np.amax(a) # Maximum of the flattened array np.amax(a, axis=0) # Maxima along the first axis np.amax(a, axis=1) # Maxima along the second axis b = np.arange(5, dtype=np.float) # b[2] = np.NaN np.amax(b) np.nanmax(b) a = np.arange(4).reshape((2,2)) np.amin(a) # Minimum of the flattened array np.amin(a, axis=0) # Minima along the first axis np.amin(a, axis=1) # Minima along the second axis b = np.arange(5, dtype=np.float) # b[2] = np.NaN np.amin(b) np.nanmin(b) a = np.zeros((7,4,5)) a.shape[0] np.alen(a) x = np.array([536870910, 536870910, 536870910, 536870910]) np.prod(x) #random np.prod([]) np.prod([1.,2.]) np.prod([[1.,2.],[3.,4.]]) np.prod([[1.,2.],[3.,4.]], axis=1) x = np.array([1, 2, 3], dtype=np.uint8) # np.prod(x).dtype == np.uint x = np.array([1, 2, 3], dtype=np.int8) # np.prod(x).dtype == np.int a = np.array([1,2,3]) np.cumprod(a) # intermediate results 1, 1*2 a = np.array([[1, 2, 3], [4, 5, 6]]) np.cumprod(a, dtype=float) # specify type of output np.cumprod(a, axis=0) np.cumprod(a,axis=1) np.ndim([[1,2,3],[4,5,6]]) np.ndim(np.array([[1,2,3],[4,5,6]])) np.ndim(1) a = np.array([[1,2,3],[4,5,6]]) np.size(a) np.size(a,1) np.size(a,0) np.around([0.37, 1.64]) np.around([0.37, 1.64], decimals=1) np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value np.around([1,2,3,11], decimals=1) # ndarray of ints is returned np.around([1,2,3,11], decimals=-1) a = np.array([[1, 2], [3, 4]]) np.mean(a) np.mean(a, axis=0) np.mean(a, axis=1) a = np.zeros((2, 512*512), dtype=np.float32) a[0, :] = 1.0 a[1, :] = 0.1 np.mean(a) np.mean(a, dtype=np.float64) a = np.array([[1, 2], [3, 4]]) np.std(a) np.std(a, axis=0) np.std(a, axis=1) a = np.zeros((2, 512*512), dtype=np.float32) a[0, :] = 1.0 a[1, :] = 0.1 np.std(a) np.std(a, dtype=np.float64) a = np.array([[1, 2], [3, 4]]) np.var(a) np.var(a, axis=0) np.var(a, axis=1) a = np.zeros((2, 512*512), dtype=np.float32) a[0, :] = 1.0 a[1, :] = 0.1 np.var(a) np.var(a, dtype=np.float64)
def get(self, vect, name): idxs, shape = self.idxs_and_shapes[name] return np.reshape(vect[idxs], shape)
def test_fromnumeric(): # Functions # 'alen', 'all', 'alltrue', 'amax', 'amin', 'any', 'argmax', # 'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip', # 'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean', # 'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put', # 'rank', 'ravel', 'repeat', 'reshape', 'resize', 'round_', # 'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze', # 'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var', a = [4, 3, 5, 7, 6, 8] indices = [0, 1, 4] np.take(a, indices) a = np.array(a) # a[indices] np.take(a, [[0, 1], [2, 3]]) a = np.zeros((10, 2)) b = a.T a = np.arange(6).reshape((3, 2)) np.reshape(a, (2, 3)) # C-like index ordering np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape np.reshape(a, (2, 3), order='F') # Fortran-like index ordering np.reshape(np.ravel(a, order='F'), (2, 3), order='F') a = np.array([[1, 2, 3], [4, 5, 6]]) np.reshape(a, 6) np.reshape(a, 6, order='F') np.reshape(a, (3, -1)) # the unspecified value is inferred to be 2 choices = [[0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33]] np.choose([2, 3, 1, 0], choices) np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1) np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4) a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]] choices = [-10, 10] np.choose(a, choices) a = np.array([0, 1]).reshape((2, 1, 1)) c1 = np.array([1, 2, 3]).reshape((1, 3, 1)) c2 = np.array([-1, -2, -3, -4, -5]).reshape((1, 1, 5)) np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2 np.repeat(3, 4) x = np.array([[1, 2], [3, 4]]) np.repeat(x, 2) np.repeat(x, 3, axis=1) np.repeat(x, [1, 2], axis=0) a = np.arange(5) np.put(a, [0, 2], [-44, -55]) a = np.arange(5) np.put(a, 22, -5, mode='clip') x = np.array([[1, 2, 3]]) np.swapaxes(x, 0, 1) x = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) np.swapaxes(x, 0, 2) x = np.arange(4).reshape((2, 2)) np.transpose(x) x = np.ones((1, 2, 3)) np.transpose(x, (1, 0, 2)).shape a = np.array([3, 4, 2, 1]) np.partition(a, 3) np.partition(a, (1, 3)) x = np.array([3, 4, 2, 1]) x[np.argpartition(x, 3)] x[np.argpartition(x, (1, 3))] x = [3, 4, 2, 1] np.array(x)[np.argpartition(x, 3)] a = np.array([[1, 4], [3, 1]]) np.sort(a) # sort along the last axis np.sort(a, axis=None) # sort the flattened array np.sort(a, axis=0) # sort along the first axis dtype = [('name', 'S10'), ('height', float), ('age', int)] values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38), ('Galahad', 1.7, 38)] a = np.array(values, dtype=dtype) # create a structured array np.sort(a, order='height') # doctest: +SKIP np.sort(a, order=['age', 'height']) # doctest: +SKIP x = np.array([3, 1, 2]) np.argsort(x) x = np.array([[0, 3], [2, 2]]) np.argsort(x, axis=0) np.argsort(x, axis=1) x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')]) np.argsort(x, order=('x', 'y')) np.argsort(x, order=('y', 'x')) a = np.arange(6).reshape(2, 3) np.argmax(a) np.argmax(a, axis=0) np.argmax(a, axis=1) b = np.arange(6) b[1] = 5 np.argmax(b) # Only the first occurrence is returned. a = np.arange(6).reshape(2, 3) np.argmin(a) np.argmin(a, axis=0) np.argmin(a, axis=1) b = np.arange(6) b[4] = 0 np.argmin(b) # Only the first occurrence is returned. np.searchsorted([1, 2, 3, 4, 5], 3) np.searchsorted([1, 2, 3, 4, 5], 3, side='right') np.searchsorted([1, 2, 3, 4, 5], [-10, 10, 2, 3]) a = np.array([[0, 1], [2, 3]]) np.resize(a, (2, 3)) np.resize(a, (1, 4)) np.resize(a, (2, 4)) x = np.array([[[0], [1], [2]]]) x.shape np.squeeze(x).shape np.squeeze(x, axis=(2, )).shape a = np.arange(4).reshape(2, 2) a = np.arange(8).reshape(2, 2, 2) a a[:, :, 0] # main diagonal is [0 6] a[:, :, 1] # main diagonal is [1 7] np.trace(np.eye(3)) a = np.arange(8).reshape((2, 2, 2)) np.trace(a) a = np.arange(24).reshape((2, 2, 2, 3)) np.trace(a).shape x = np.array([[1, 2, 3], [4, 5, 6]]) np.ravel(x) x.reshape(-1) np.ravel(x, order='F') np.ravel(x.T) np.ravel(x.T, order='A') a = np.arange(3)[::-1] a # a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a x = np.eye(3) np.nonzero(x) x[np.nonzero(x)] np.transpose(np.nonzero(x)) a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) a > 3 np.nonzero(a > 3) np.shape(np.eye(3)) np.shape([[1, 2]]) np.shape([0]) np.shape(0) a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')]) np.shape(a) a.shape a = np.array([[1, 2], [3, 4], [5, 6]]) np.compress([0, 1], a, axis=0) np.compress([False, True, True], a, axis=0) np.compress([False, True], a, axis=1) np.compress([False, True], a) a = np.arange(10) np.clip(a, 1, 8) np.clip(a, 3, 6, out=a) a = np.arange(10) np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8) np.sum([]) np.sum([0.5, 1.5]) np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32) np.sum([[0, 1], [0, 5]]) np.sum([[0, 1], [0, 5]], axis=0) np.sum([[0, 1], [0, 5]], axis=1) # np.ones(128, dtype=np.int8).sum(dtype=np.int8) # np.any([[True, False], [True, True]]) # np.any([[True, False], [False, False]], axis=0) # np.any([-1, 0, 5]) # np.any(np.nan) # np.all([[True,False],[True,True]]) # np.all([[True,False],[True,True]], axis=0) # np.all([-1, 4, 5]) # np.all([1.0, np.nan]) a = np.array([[1, 2, 3], [4, 5, 6]]) np.cumsum(a) np.cumsum(a, dtype=float) # specifies type of output value(s) np.cumsum(a, axis=0) # sum over rows for each of the 3 columns np.cumsum(a, axis=1) # sum over columns for each of the 2 rows x = np.arange(4).reshape((2, 2)) np.ptp(x, axis=0) np.ptp(x, axis=1) a = np.arange(4).reshape((2, 2)) np.amax(a) # Maximum of the flattened array np.amax(a, axis=0) # Maxima along the first axis np.amax(a, axis=1) # Maxima along the second axis b = np.arange(5, dtype=np.float) # b[2] = np.NaN np.amax(b) np.nanmax(b) a = np.arange(4).reshape((2, 2)) np.amin(a) # Minimum of the flattened array np.amin(a, axis=0) # Minima along the first axis np.amin(a, axis=1) # Minima along the second axis b = np.arange(5, dtype=np.float) # b[2] = np.NaN np.amin(b) np.nanmin(b) a = np.zeros((7, 4, 5)) a.shape[0] np.alen(a) x = np.array([536870910, 536870910, 536870910, 536870910]) np.prod(x) #random np.prod([]) np.prod([1., 2.]) np.prod([[1., 2.], [3., 4.]]) np.prod([[1., 2.], [3., 4.]], axis=1) x = np.array([1, 2, 3], dtype=np.uint8) # np.prod(x).dtype == np.uint x = np.array([1, 2, 3], dtype=np.int8) # np.prod(x).dtype == np.int a = np.array([1, 2, 3]) np.cumprod(a) # intermediate results 1, 1*2 a = np.array([[1, 2, 3], [4, 5, 6]]) np.cumprod(a, dtype=float) # specify type of output np.cumprod(a, axis=0) np.cumprod(a, axis=1) np.ndim([[1, 2, 3], [4, 5, 6]]) np.ndim(np.array([[1, 2, 3], [4, 5, 6]])) np.ndim(1) a = np.array([[1, 2, 3], [4, 5, 6]]) np.size(a) np.size(a, 1) np.size(a, 0) np.around([0.37, 1.64]) np.around([0.37, 1.64], decimals=1) np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value np.around([1, 2, 3, 11], decimals=1) # ndarray of ints is returned np.around([1, 2, 3, 11], decimals=-1) a = np.array([[1, 2], [3, 4]]) np.mean(a) np.mean(a, axis=0) np.mean(a, axis=1) a = np.zeros((2, 512 * 512), dtype=np.float32) a[0, :] = 1.0 a[1, :] = 0.1 np.mean(a) np.mean(a, dtype=np.float64) a = np.array([[1, 2], [3, 4]]) np.std(a) np.std(a, axis=0) np.std(a, axis=1) a = np.zeros((2, 512 * 512), dtype=np.float32) a[0, :] = 1.0 a[1, :] = 0.1 np.std(a) np.std(a, dtype=np.float64) a = np.array([[1, 2], [3, 4]]) np.var(a) np.var(a, axis=0) np.var(a, axis=1) a = np.zeros((2, 512 * 512), dtype=np.float32) a[0, :] = 1.0 a[1, :] = 0.1 np.var(a) np.var(a, dtype=np.float64)
def forward(self, inputs, *args): shape = tuple([inputs.shape[0]] + list(self.shape)) return np.reshape(inputs, shape)
def set(self, vect, name, new): """Set the value of name in vect as new and return new array """ idxs, shape = self.idxs_and_shapes[name] vect[idxs] = np.reshape(new, -1) return vect
def test_numeric(): # 'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc', # 'arange', 'array', 'zeros', 'count_nonzero', 'empty', 'broadcast', # 'dtype', 'fromstring', 'fromfile', 'frombuffer', 'int_asbuffer', # 'where', 'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose', # 'lexsort', 'set_numeric_ops', 'can_cast', 'promote_types', # 'min_scalar_type', 'result_type', 'asarray', 'asanyarray', # 'ascontiguousarray', 'asfortranarray', 'isfortran', 'empty_like', # 'zeros_like', 'ones_like', 'correlate', 'convolve', 'inner', 'dot', # 'einsum', 'outer', 'vdot', 'alterdot', 'restoredot', 'roll', # 'rollaxis', 'moveaxis', 'cross', 'tensordot', 'array2string', # 'get_printoptions', 'set_printoptions', 'array_repr', 'array_str', # 'set_string_function', 'little_endian', 'require', 'fromiter', # 'array_equal', 'array_equiv', 'indices', 'fromfunction', 'isclose', 'load', # 'loads', 'isscalar', 'binary_repr', 'base_repr', 'ones', 'identity', # 'allclose', 'compare_chararrays', 'putmask', 'seterr', 'geterr', # 'setbufsize', 'getbufsize', 'seterrcall', 'geterrcall', 'errstate', # 'flatnonzero', 'Inf', 'inf', 'infty', 'Infinity', 'nan', 'NaN', 'False_', # 'True_', 'bitwise_not', 'full', 'full_like', 'matmul' x = np.arange(6) x = x.reshape((2, 3)) np.zeros_like(x) y = np.arange(3, dtype=np.float) np.zeros_like(y) np.ones(5) np.ones((5, ), dtype=np.int) np.ones((2, 1)) s = (2, 2) np.ones(s) x = np.arange(6) x = x.reshape((2, 3)) np.ones_like(x) y = np.arange(3, dtype=np.float) np.ones_like(y) np.full((2, 2), np.inf) x = np.arange(6, dtype=np.int) np.full_like(x, 1) np.full_like(x, 0.1) np.full_like(y, 0.1) np.count_nonzero(np.eye(4)) np.count_nonzero([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]]) np.count_nonzero([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]], axis=0) np.count_nonzero([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]], axis=1) a = [1, 2] np.asarray(a) a = np.array([1, 2]) np.asarray(a) is a a = np.array([1, 2], dtype=np.float32) np.asarray(a, dtype=np.float32) is a np.asarray(a, dtype=np.float64) is a np.asarray(a) is a np.asanyarray(a) is a a = [1, 2] np.asanyarray(a) np.asanyarray(a) is a x = np.arange(6).reshape(2, 3) np.ascontiguousarray(x, dtype=np.float32) x = np.arange(6).reshape(2, 3) y = np.asfortranarray(x) x = np.arange(6).reshape(2, 3) y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F']) a = np.array([[1, 2, 3], [4, 5, 6]], order='C') np.isfortran(a) b = np.array([[1, 2, 3], [4, 5, 6]], order='FORTRAN') np.isfortran(b) a = np.array([[1, 2, 3], [4, 5, 6]], order='C') np.isfortran(a) b = a.T np.isfortran(b) np.isfortran(np.array([1, 2], order='FORTRAN')) x = np.arange(6).reshape(2, 3) np.argwhere(x > 1) x = np.arange(-2, 3) np.flatnonzero(x) np.correlate([1, 2, 3], [0, 1, 0.5]) np.correlate([1, 2, 3], [0, 1, 0.5], "same") np.correlate([1, 2, 3], [0, 1, 0.5], "full") np.correlate([1 + 1j, 2, 3 - 1j], [0, 1, 0.5j], 'full') np.correlate([0, 1, 0.5j], [1 + 1j, 2, 3 - 1j], 'full') np.convolve([1, 2, 3], [0, 1, 0.5]) np.convolve([1, 2, 3], [0, 1, 0.5], 'same') np.convolve([1, 2, 3], [0, 1, 0.5], 'valid') rl = np.outer(np.ones((5, )), np.linspace(-2, 2, 5)) # im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,))) # grid = rl + im x = np.array(['a', 'b', 'c'], dtype=object) np.outer(x, [1, 2, 3]) a = np.arange(60.).reshape(3, 4, 5) b = np.arange(24.).reshape(4, 3, 2) c = np.tensordot(a, b, axes=([1, 0], [0, 1])) c.shape # A slower but equivalent way of computing the same... d = np.zeros((5, 2)) a = np.array(range(1, 9)) A = np.array(('a', 'b', 'c', 'd'), dtype=object) x = np.arange(10) np.roll(x, 2) x2 = np.reshape(x, (2, 5)) np.roll(x2, 1) np.roll(x2, 1, axis=0) np.roll(x2, 1, axis=1) a = np.ones((3, 4, 5, 6)) np.rollaxis(a, 3, 1).shape np.rollaxis(a, 2).shape np.rollaxis(a, 1, 4).shape x = np.zeros((3, 4, 5)) np.moveaxis(x, 0, -1).shape np.moveaxis(x, -1, 0).shape np.transpose(x).shape np.moveaxis(x, [0, 1], [-1, -2]).shape np.moveaxis(x, [0, 1, 2], [-1, -2, -3]).shape x = [1, 2, 3] y = [4, 5, 6] np.cross(x, y) x = [1, 2] y = [4, 5, 6] np.cross(x, y) x = [1, 2, 0] y = [4, 5, 6] np.cross(x, y) x = [1, 2] y = [4, 5] np.cross(x, y) x = np.array([[1, 2, 3], [4, 5, 6]]) y = np.array([[4, 5, 6], [1, 2, 3]]) np.cross(x, y) np.cross(x, y, axisc=0) x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) y = np.array([[7, 8, 9], [4, 5, 6], [1, 2, 3]]) np.cross(x, y) np.cross(x, y, axisa=0, axisb=0) # np.array_repr(np.array([1,2])) # np.array_repr(np.ma.array([0.])) # np.array_repr(np.array([], np.int32)) x = np.array([1e-6, 4e-7, 2, 3]) # np.array_repr(x, precision=6, suppress_small=True) # np.array_str(np.arange(3)) a = np.arange(10) x = np.arange(4) np.set_string_function(lambda x: 'random', repr=False) grid = np.indices((2, 3)) grid.shape grid[0] # row indices grid[1] # column indices x = np.arange(20).reshape(5, 4) row, col = np.indices((2, 3)) x[row, col] np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int) np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int) np.isscalar(3.1) np.isscalar([3.1]) np.isscalar(False) # np.binary_repr(3) # np.binary_repr(-3) # np.binary_repr(3, width=4) # np.binary_repr(-3, width=3) # np.binary_repr(-3, width=5) # np.base_repr(5) # np.base_repr(6, 5) # np.base_repr(7, base=5, padding=3) # np.base_repr(10, base=16) # np.base_repr(32, base=16) np.identity(3) np.allclose([1e10, 1e-7], [1.00001e10, 1e-8]) np.allclose([1e10, 1e-8], [1.00001e10, 1e-9]) np.allclose([1e10, 1e-8], [1.0001e10, 1e-9]) # np.allclose([1.0, np.nan], [1.0, np.nan]) # np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True) np.isclose([1e10, 1e-7], [1.00001e10, 1e-8]) np.isclose([1e10, 1e-8], [1.00001e10, 1e-9]) np.isclose([1e10, 1e-8], [1.0001e10, 1e-9]) # np.isclose([1.0, np.nan], [1.0, np.nan]) # np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True) np.array_equal([1, 2], [1, 2]) np.array_equal(np.array([1, 2]), np.array([1, 2])) np.array_equal([1, 2], [1, 2, 3]) np.array_equal([1, 2], [1, 4]) np.array_equiv([1, 2], [1, 2]) np.array_equiv([1, 2], [1, 3]) np.array_equiv([1, 2], [[1, 2], [1, 2]]) np.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]]) np.array_equiv([1, 2], [[1, 2], [1, 3]])
def get(self, vect, name): """Return the values of the variable with name in vect with original shape""" idxs, shape = self.idxs_and_shapes[name] return np.reshape(vect[idxs], shape) # reshape to original shape